'scaling' 검색 결과

Adapt machine learning training to run across multiple GPUs or machines with Ray Train. Covers changes to existing code, comparing training settings with Ray Tune, and saving checkpoints so training can resume after failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adapting existing PyTorch code for multiple GPUs
• Training HuggingFace models across multiple machines
• Comparing learning rates and batch sizes with Ray Tune

Train smaller language models using knowledge distillation, where a larger model supplies examples or prediction targets. This skill covers training losses, generated training data, parameter choices, and output comparisons to support model compression and specialized student models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a smaller model from a larger teacher
• Fine-tuning with teacher-generated responses
• Distilling domain knowledge into a specialized model

Tune cognee ingestion speed, query response time, and LLM costs by adjusting concurrency, text chunk sizes, request limits, and model choices. Estimate ingestion costs before processing and assess options for background work and deployment scaling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimating costs before importing documents
• Adjusting ingestion speed and request limits
• Reducing query response time

Plan technology investments and engineering team growth using structured assessments. Evaluate technical debt, compare building versus buying, document architecture choices, and define engineering health metrics to produce prioritized plans grounded in costs, risks, and business needs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritize technical debt fixes by impact and effort
• Plan engineering hiring, timing, and budget
• Compare building internally with buying a service

Inspect Google Cloud Filestore usage and free space, then propose capacity increases or decreases based on configured thresholds and storage tier limits. Apply resizing after confirmation to address low space or reduce excess capacity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check free space across Filestore instances
• Increase capacity when storage is running low
• Reduce excess capacity on eligible storage tiers

Deploy AI models on GKE with suitable GPUs or TPUs and model servers. Generate deployment configuration, adjust automatic scaling, and tune inference—the process of producing model responses—for memory use, throughput, and response time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying Gemma or Llama on GKE
• Choosing GPUs or TPUs for model serving
• Configuring automatic scaling for LLM requests

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Build search and recommendation features with Pinecone, a managed database for numerical representations of content. Covers data loading, combined meaning and keyword search, filtering by attributes, and separating data by user.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build document retrieval for AI answers
• Combine meaning and keyword search
• Separate search data by user

Guides fine-tuning and evaluation of OpenVLA-OFT robot models that turn images and language into actions. Covers LIBERO simulation, ALOHA deployment, LoRA adaptation that updates a small set of parameters, and troubleshooting action scaling or configuration mismatches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Evaluate pretrained models in LIBERO
• Fine-tune robot action models with LoRA
• Deploy server-client inference for ALOHA

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Helps adapt machine learning training to multiple GPUs and machines with Ray Train. Covers resource allocation, recovery from saved training states, and Ray Tune searches that compare settings such as learning rate and batch size.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adapt existing PyTorch training to multiple GPUs
• Run HuggingFace training across multiple machines
• Compare learning rates and batch sizes with Ray Tune

Build real-time messaging with WebSocket or Socket.IO, including authentication, chat rooms, online status, and reconnection. Provides server and client code, plus guidance on using Redis to share messages across multiple servers and monitor connections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building chat and live-update features
• Managing rooms and online user status
• Handling reconnection and queued messages

Compare ways to distribute AI inference across multiple instances, including replicas and separate input-processing and generation stages. Estimate capacity, throughput, delays, state-transfer costs, and recovery overhead to support routing and scaling decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare replicas with separate inference stages
• Plan routing around compute capacity and cached state
• Evaluate expert placement and load imbalance

Design reusable SEO page templates from a niche and data source. Get title patterns, page sections, internal linking rules, structured data for search engines, and requirements that keep each page useful and distinct.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning location pages for a service
• Designing product comparison page templates
• Building industry-specific software recommendation pages

Plan and audit preprocessing for grouped time-series data, such as matches or patient records. Check missing-value handling, scaling scope, feature meaning, and data leakage, where information unavailable at prediction time enters model inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning missing-value handling within each group
• Choosing within-group or global scaling
• Auditing preprocessing code for data leakage

Configure Jetson CPU, GPU, and memory-controller clock frequencies before flashing an image. Edit firmware settings and the boot script to fix or limit frequencies, change frequency-control policies, and enable or disable automatic frequency scaling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing fixed CPU or GPU frequencies for performance tests
• Setting maximum or minimum device clock frequencies
• Enabling or disabling automatic memory-controller frequency scaling

Provisions, scales, and operates Amazon EC2 virtual-machine workloads: instance-type selection (Graviton/Arm64, burstable T credits, GPU, instance store vs EBS), launch templates, Auto Scaling groups (scaling policies, instance refresh, mixed instances, Spot, warm pools, lifecycle hooks), IMDSv2, placement groups, Elastic IPs, AMI lifecycle, and Systems Manager fleet operations (Session Manager, Run Command, Patch Manager). Applies to EC2 instance and fleet questions, InsufficientInstanceCapacity, CPU-credit/surplus charges, IMDSv2 401s, instances stuck in Pending:Wait, ASG not replacing unhealthy instances, status-check failures, SSH refused/timed out, or instances missing as SSM managed nodes. For a single secure instance launch, the launching-ec2-instance-with-best-practices skill is more appropriate; for instance profiles, see setting-up-ec2-instance-profiles; for Image Builder, see amazon-ec2-image-builder. Does NOT cover Lambda, ECS/Fargate, EKS, VPC/ALB/NLB design, or IAM policy authoring.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze and optimize system throughput including request handling, data processing, and resource utilization. Use when identifying capacity limits or evaluating scaling strategies. Trigger with phrases like "analyze throughput", "optimize capacity", or "identify bottlenecks".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Execute this skill enables AI assistant to analyze capacity requirements and plan for future growth. it uses the capacity-planning-analyzer plugin to assess current utilization, forecast growth trends, and recommend scaling strategies. use this skill when the u... Use when analyzing code or data. Trigger with phrases like 'analyze', 'review', or 'examine'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and build game UI/UX — HUDs, menus, and overlays — that survive every screen: anchor- based responsive layout, resolution/aspect scaling and safe areas, keyboard/gamepad focus navigation, a screen/menu state stack, and event-driven (not polled) HUD updates. Engine- neutral patterns that pair with the detected engine's UI skill. Use when the user mentions HUD, health bar, main menu, pause menu, settings screen, UI layout, anchors, UI scaling, aspect ratio, safe area, controller/keyboard menu navigation, or wiring UI to game state.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/scaling choices, robust color mapping, optimal leaf ordering, and ComplexHeatmap/pheatmap/seaborn rendering. Covers the ward.D vs ward.D2 trap, the row-vs-column scaling decision, multi-track annotations, oncoPrint, and raster rendering for large matrices. Use when visualizing expression patterns across samples or identifying co-regulated clusters.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user wants to validate product-market fit, measure PMF, or plan before scaling. Also use when the user mentions "PMF," "product-market fit," "product market fit," "Sean Ellis test," "very disappointed," "vitamin vs painkiller," "PMF validation," "premature scaling," or "validate before scale." For GTM after validation, use gtm-strategy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI). Triggers on: Lambda Managed Instances, LMI, capacity provider, multi-concurrency Lambda, dedicated instance Lambda, EC2-backed Lambda, cold start elimination, Graviton Lambda, instance type for Lambda, scheduled scaling for LMI, Lambda cost optimization with Reserved Instances or Savings Plans. Also trigger when users describe high-volume predictable workloads seeking cost savings, want to scale LMI capacity on a schedule, or compare Lambda vs EC2 for steady-state traffic. For standard Lambda without LMI, use the aws-lambda skill instead.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert patterns for scaling mobile games to desktop including mouse/keyboard controls, increased resolution and graphical fidelity, expanded UI layouts, settings menus, window management, and platform-specific features. Use when creating desktop ports or cross-platform releases. Trigger keywords: mouse_controls, keyboard_shortcuts, resolution_scaling, graphics_settings, fullscreen_toggle, window_modes, Steam_integration, desktop_optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.