'gpu' 검색 결과

Plan how to split AI models and workloads across multiple GPUs. Compare memory needs, computation, communication, and waiting time to estimate completion times and choose a layout suited to response time, throughput, or cost goals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running models that exceed a single GPU's memory
• Comparing ways to split work across GPUs
• Estimating communication costs within and between servers

Convert single-node training or inference commands into multi-node Slurm batch jobs. Choose an appropriate process launch method, configure shared storage and containers, and investigate communication timeouts, launch errors, and GPU memory shortages.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting a single-node command into a multi-node sbatch job
• Investigating NCCL communication timeouts
• Estimating GPU memory needs for MoE models

Check and change NVIDIA display and GPU settings through natural-language requests using G-Assist tools. Inspect current settings, adjust supported display controls and frame-rate limits, or plot GPU and CPU metrics on the current machine.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether G-SYNC or V-Sync is enabled
• Change display resolution or refresh rate
• Adjust brightness and color on a supported monitor

vLLM으로 대규모 언어 모델을 API로 제공하거나 많은 입력을 일괄 처리하도록 돕습니다. 배포와 성능 모니터링, 메모리 절약, 응답 속도 조정도 다룹니다.

• OpenAI 호환 API를 제공할 때
• 많은 프롬프트를 일괄 처리할 때
• 제한된 GPU 메모리로 모델을 실행할 때

가사와 곡 분위기 및 악기 태그로 로컬에서 음악을 생성합니다. 설치와 입력 준비, GPU 메모리 절약 설정부터 MP3 저장까지 안내합니다.

• 직접 쓴 가사를 곡으로 만들기
• 태그로 곡 분위기와 악기 시험하기
• 로컬 환경에서 음악 생성하기

Wi-Fi 측정 데이터로 RuView 모델을 훈련하고 평가합니다. 자세 추정, 환경 적응, GPU 훈련과 Hugging Face 모델 공개를 다룹니다.

• 카메라와 레이블 없이 자세 추정 학습하기
• 카메라와 Wi-Fi 대응 데이터로 학습하기
• 다른 방이나 환경에 모델 적응시키기

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Analyze LLM serving traces to identify GPU time usage, opportunities to run operations concurrently, and known patterns for combining operations. Produce three consistent tables from saved traces or live captures on supported servers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identify GPU operations that dominate inference time
• Compare input processing and token generation
• Trace GPU operations back to Python code

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Organize PyTorch training code with LightningModule and Trainer. Follow examples for validation, logging, model saving, and distributed training across multiple GPUs, with callbacks that add monitoring or stop training when progress stalls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting an existing PyTorch training loop
• Adding validation, logs, and model checkpoints
• Configuring training across multiple GPUs

Run batches of machine learning experiments on remote GPUs over SSH, varying settings and random seeds. The skill schedules jobs, tracks progress, retries GPU memory failures, and cleans up leftover sessions before starting subsequent batches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run experiments across multiple random seeds
• Test combinations of model settings
• Chain teacher training and student distillation

Compress large language models to 4-bit weights to reduce GPU memory needs and accelerate text generation. Covers loading compressed models, converting your own models, choosing settings, and preparing memory-efficient fine-tuning with transformers and PEFT.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run language models with limited GPU memory
• Convert your own model to 4-bit weights
• Compare compression settings for quality and speed

Helps reduce GPU memory use by loading large language models in 4-bit or 8-bit formats, a technique called quantization. Covers bitsandbytes configuration, memory checks, QLoRA fine-tuning, and training with memory-saving optimizers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load a larger model within available GPU memory
• Compare 4-bit and 8-bit quantization settings
• Fine-tune a quantized model with QLoRA

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Recommends network-port assignments and environment settings for NVSHMEM using the compute node’s actual GPU and network topology. Helps select adapters, evaluate multiple-port options, and identify missing evidence before producing an exact configuration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing network ports closest to each GPU
• Checking multi-NIC options for an NVSHMEM version
• Diagnosing unexpected port selection

Diagnose GPU bugs in CUDA applications with NVIDIA Compute Sanitizer. Select the appropriate tool for memory access errors, shared-memory races, uninitialized reads, or synchronization faults, then build for source locations and interpret the diagnostic report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locate illegal or out-of-bounds GPU memory access
• Investigate inconsistent results caused by shared-memory races
• Find reads of uninitialized GPU memory

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

Optimize MATLAB design files for GPU Coder to generate faster CUDA code. Iteratively profiles, rewrites, and benchmarks until performance targets are met or diagnostics are resolved. Use when asked to: optimize for GPU Coder, improve GPU codegen performance, profile generated GPU/CUDA code, profile GPU MEX, fix gpuPerformanceAnalyzer diagnostics, speed up GPU MEX, reduce GPU memory transfers, improve kernel parallelism, rewrite MATLAB for CUDA, or run gpuPerformanceAnalyzer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Axolotl로 대규모 언어 모델을 추가 학습하는 데 필요한 YAML 설정, 데이터 형식과 학습 방법을 안내합니다. 여러 GPU의 설정과 학습 오류, 속도 문제 조사에도 활용합니다.

• 추가 학습용 YAML 설정 작성
• LoRA와 QLoRA 학습 방법 확인
• 데이터 형식과 API 사용법 확인

Unsloth로 언어 모델을 추가 학습하도록 돕습니다. LoRA와 QLoRA 구현, 기능과 API 확인, 코드 오류 조사, GPU 메모리를 줄이는 학습 방법 탐색을 지원합니다.

• LoRA와 QLoRA 추가 학습 시작
• Unsloth 기능과 API 확인
• 학습 코드의 오류 조사

Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Fine-tune large language models with LoRA and QLoRA while limiting GPU memory use. Guides training settings and adapter management, using small sets of task-specific weights that can be saved, switched, or merged into a base model.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an LLM with limited GPU memory
• Choosing LoRA settings and training layers
• Switching task-specific adapters on one base model