'speed optimization' 검색 결과

Implement and troubleshoot large-model training with DeepSpeed across multiple GPUs. The skill covers memory optimization, parallel training, and numerical precision choices, alongside examples for storage transfers and models that divide work among specialized components.

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• Review settings for distributed model training
• Tune transfers between NVMe storage and tensors
• Configure training with specialized model components

Helps configure and troubleshoot DeepSpeed for training large models across multiple devices. Covers memory optimization, parallel training, and DeepNVMe storage transfers, with examples for tuning data movement and configuring models that divide work among specialized components.

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• Configuring distributed training for large models
• Tuning tensor transfers to NVMe storage
• Setting up models with specialized expert components

Train Mixture of Experts models, which activate selected specialist networks for each input. The skill guides architecture, input routing, workload balancing, and GPU distribution using DeepSpeed or HuggingFace, with examples for training configuration and inference optimization.

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• Designing a model that activates only selected expert networks
• Balancing workloads across expert networks
• Distributing experts across multiple GPUs

현재 성능 측정, 가설별 변경안 비교, 정확성 검증으로 최적화를 진행합니다. 시간과 비용 한도 안에서 가장 빠르고 안전한 안을 고르고 재실행 절차를 남깁니다.

• 느린 작업을 측정하며 속도 개선
• 같은 입력 조건에서 구현안 비교
• 응답 시간, 처리량, 실행 비용 평가

Integrate Flash Attention into transformer models to reduce processing time and GPU memory use for long sequences. Covers PyTorch integration, advanced attention options, and benchmarks that compare speed, memory use, and output accuracy.

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• Speeding up transformer training on long sequences
• Reducing GPU memory use during attention
• Adding Flash Attention to a PyTorch model

Build and train Mixture of Experts models, which activate selected specialist networks for each input. Use DeepSpeed or HuggingFace workflows to configure routing, balance workloads, distribute experts across GPUs, and optimize inference.

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• Designing MoE models for a limited compute budget
• Implementing Mixtral-style expert routing
• Adjusting expert workloads and training stability

Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.

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• Compare performance before and after v3 optimization
• Evaluate vector search speed and memory use
• Track performance regressions over time

Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.

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• Compare performance before and after v3 optimization
• Evaluate vector search speed and memory use
• Track performance regressions over time

Expert at diagnosing and fixing performance bottlenecks across the stack. Covers Core Web Vitals, database optimization, caching strategies, bundle optimization, and performance monitoring. Knows when to measure vs optimize. Use when "slow page load, performance optimization, core web vitals, bundle size, lighthouse score, database slow, memory leak, optimize performance, speed up, reduce load time, performance, optimization, core-web-vitals, caching, profiling, bundle-size, database" mentioned.

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Analyze GTmetrix (and PageSpeed) reports for WordPress sites and produce a prioritized optimization plan, a custom WPCode-ready PHP optimization snippet, and a self-contained HTML summary report. Covers PDF report extraction with pypdf, live-page cross-referencing, WordPress-specific fix mapping (Elementor, WooCommerce, Breeze, reCAPTCHA, Google-tag duplication, WebP conversion), and staging-test discipline. Use when asked to review GTmetrix reports, optimize page speed or Core Web Vitals, create a custom performance optimization snippet, or produce an optimization report for a WordPress site.

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Integrate Flash Attention into Transformer models to speed up attention, the calculation that relates input elements, while reducing GPU memory use. Covers PyTorch integration, performance measurement, output comparison, and a separate H100 optimization path.

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• Optimize training with long input sequences
• Reduce memory use during long-context inference
• Measure speed and compare model outputs

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

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• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.

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• Reduce memory use in an AgentDB application
• Tune search speed while preserving accuracy
• Plan settings for millions of vectors

Optimize transformer attention, the calculation that relates tokens to one another, using Flash Attention. The skill guides PyTorch integration, GPU memory reduction, performance measurement, and output comparisons for training or inference with long sequences.

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• Reducing GPU memory use with long sequences
• Integrating Flash Attention into PyTorch models
• Comparing runtime and outputs before and after changes

Improve a web page’s loading speed, response to user actions, and visual stability. The skill guides targeted fixes for LCP, INP, and CLS, with code examples, measurement methods, and checklists for each metric.

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• Speeding up the display of a page’s main image
• Reducing delays after clicks and keyboard input
• Preventing layout shifts from images, ads, and fonts

Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.

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Diagnose and fix web performance issues including Core Web Vitals (LCP, INP, CLS), bundle size, asset optimization, render performance, and runtime efficiency. Use this skill whenever the user wants to improve page speed, fix Core Web Vitals, optimize assets, reduce bundle size, debug slow renders, or systematically improve a site's performance. Triggers on performance, page speed, Core Web Vitals, LCP, INP, CLS, FID, TTFB, bundle size, code splitting, image optimization, lazy loading, render blocking, slow page, performance audit, Lighthouse score. Also triggers when traffic or conversion is dropping due to perceived slowness.

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Landing page audit and optimization for conversion. Covers above-the-fold design, value propositions, CTAs, social proof placement, form design, page speed, and mobile optimization. Use when the user asks about landing page optimization, conversion rate improvement, page audits, or CTA optimization.

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

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Audit web pages for performance optimization following PageSpeed Insights guidelines. Use when analyzing page performance, optimizing web applications, reviewing performance metrics, implementing Core Web Vitals improvements, or when the user mentions page speed, performance optimization, Lighthouse scores, or Core Web Vitals.

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Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.

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Audit a website for problems affecting search visibility, from search engine access and indexing to speed, page titles, content, and links. Receive evidence-based findings and a prioritized action plan, including checks for multilingual and local business sites.

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• Investigating a drop in search traffic or rankings
• Reviewing page titles and internal links
• Checking multilingual search settings

Plan website improvements for organic search traffic through keyword research, content structure, page metadata, and technical checks. Get guidance on internal links, structured data that describes page content, loading speed, and ongoing performance monitoring.

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• Choosing keywords and planning related articles
• Reviewing titles, descriptions, and headings
• Checking structured data and indexing settings

Helps speed up LLM text generation by comparing methods that predict and verify multiple tokens at once. Covers draft model selection, Medusa prediction heads, Lookahead Decoding settings, and deployment examples for inference services.

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• Reducing chatbot response latency
• Comparing methods for faster code generation
• Selecting a small draft model for a larger LLM