“gpu”的搜索结果

使用TorchTitan和PyTorch配置大语言模型预训练，组合四类并行方式，将计算分配到多个GPU，支持单节点与多节点运行，以及训练状态的保存和恢复。

• 在单节点预训练Llama 3.1 8B
• 使用SLURM运行多节点训练
• 为H100配置Float8训练

将PyTorch训练代码整理为LightningModule和Trainer结构，配置验证、日志记录、模型保存及多GPU分布式训练。

• 迁移现有PyTorch训练代码
• 添加验证指标记录和模型保存
• 配置多GPU训练

Operate NVIDIA Nsight Graphics from the command line to capture application frames and GPU activity. Summarize performance traces, analyze existing capture files through ngfx-replay, and check installed versions and compatibility before starting an investigation.

中文简介正在准备中。当前显示英语简介。

• Check installed Nsight Graphics versions and compatibility
• Capture frames from a graphics application
• Summarize GPU activity and investigate performance bottlenecks

Measure scripts, running processes, or whole systems to find performance bottlenecks. Select suitable tools, add measurement code when needed, and report CPU, GPU, memory, and communication findings with prioritized recommendations and a record of file changes.

中文简介正在准备中。当前显示英语简介。

• Finding slow functions in a Python script
• Investigating CPU and GPU memory usage
• Measuring communication overhead across multiple GPUs

Guide large language model training with OpenRLHF using human preference feedback. Covers training method selection, distributed GPU setup, reward model training, and memory tuning, with examples for PPO, GRPO, and DPO.

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• Choosing a training method for human preference alignment
• Setting up distributed training across multiple GPUs
• Training a reward model before PPO

Use this skill when the user is building, running, or interpreting the doca/tools/gpunetio_ib_write_bw client+server benchmark — a CUDA kernel on the client posts RDMA WRITE work requests through the doca-gpunetio device-side surface to measure sustained GPU-driven WRITE bandwidth on a GPU+IB-device pair. Trigger even when the user does not explicitly mention "doca-gpunetio-ib-write-bw" or "GPUNetIO" — typical implicit phrasings include "measure WRITE BW when the GPU posts the WRs", "BW swings between runs on the same flags", "is the NIC saturated or am I CPU-bound on the CUDA kernel", "meson compile fails for the GPUNetIO bw tool", "nvidia_peermem isn't picking up my GPU buffer", or "GPU-initiated WRITE throughput vs CPU-initiated perftest". Refuse and route elsewhere for general doca-gpunetio library work, DOCA install, the GPU-initiated WRITE latency analog, the CPU-initiated upstream perftest, or application-level end-to-end throughput — those belong to other skills.

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Use this skill when the user is doing hands-on DOCA GPUNetIO programming — wiring a CUDA kernel on an NVIDIA GPU to a doca-eth queue via doca_gpu_eth_rxq / doca_gpu_eth_txq, standing up the per-CUDA-device doca_gpu context, designing the persistent CUDA kernel that drains the GPU-visible queue, running the dual capability check (DOCA cap-query plus cudaGetDeviceProperties), registering cudaMalloc pools via doca_buf_arr_create_*, or debugging DOCA_ERROR_* returns from the GPUNetIO API. Trigger even when the user does not explicitly mention "DOCA GPUNetIO" or "persistent kernel" — typical implicit phrasings include "CUDA kernel reading packets directly from the NIC", "GPU-initiated networking on BlueField", "DOCA_ERROR_DRIVER on doca_gpu_create", "nvidia_peermem not loaded", "kernel-per-packet is too slow", or "which GPU supports GPU-side packet I/O". Refuse and route elsewhere for general CUDA programming, DOCA Ethernet queue bring-up, DOCA DPA, or DOCA install — those belong to other skills.

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帮助你使用FAISS实现大规模向量相似性搜索，涵盖索引选择、保存与加载及GPU使用，并调整搜索速度、准确率和内存占用。

• 实现大规模向量相似性搜索
• 按速度、准确率和内存选择索引
• 使用GPU批量搜索

Build Python workloads on Modal for cloud execution, including GPU model training and inference, web APIs, scheduled jobs, and parallel batch processing. Configure container environments, persistent storage, and automatic scaling through the Modal SDK.

中文简介正在准备中。当前显示英语简介。

• Serving an AI model through a web API
• Running model training or fine-tuning on cloud GPUs
• Processing independent batch inputs in parallel

Run language models locally with llama.cpp on CPUs, Apple Silicon, or AMD and Intel GPUs. The skill guides model setup, memory-saving GGUF format choices, interactive chat, and serving models through an OpenAI-compatible API.

中文简介正在准备中。当前显示英语简介。

• Running a language model on a CPU-only computer
• Setting up local chat on an Apple Silicon Mac
• Choosing model quantization to fit available memory

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.

中文简介正在准备中。当前显示英语简介。

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

Launch machine learning training jobs on local or remote GPUs. The skill checks available GPU memory, synchronizes code, saves logs, and verifies startup, with optional experiment tracking and support for Vast.ai and Modal.

中文简介正在准备中。当前显示英语简介。

• Launch training on an available remote GPU
• Run experiments locally with CUDA or MPS
• Record training metrics with W&B

Deploy language model APIs and run batch generation with vLLM. Configure GPU memory use, quantization—lower precision model storage—and distribution across GPUs, then test response times and monitor how many requests the server handles.

中文简介正在准备中。当前显示英语简介。

• Deploying an OpenAI-compatible language model API
• Generating responses for a large set of prompts
• Fitting a large model into limited GPU memory

Add PyTorch FSDP2 to training scripts so model state can be split across GPUs. Covers distributed initialization, mixed precision, CPU offloading, and checkpoint saving and loading for models that exceed a single GPU's memory.

中文简介正在准备中。当前显示英语简介。

• Training a model that exceeds one GPU's memory
• Adding FSDP2 to an existing training script
• Configuring mixed precision and CPU offloading

Train or fine-tune language and vision models on Hugging Face Jobs without a local GPU. Covers dataset checks, hardware and cost planning, training monitoring, Hub storage, and GGUF conversion for local use.

中文简介正在准备中。当前显示英语简介。

• Fine-tune a language model without a local GPU
• Validate preference data before DPO training
• Estimate training costs and choose GPU hardware

Use this skill when the user is measuring GPU-kernel-initiated RDMA WRITE latency through doca-gpunetio — building and running the `gpunetio_ib_write_lat` client + server pair under `doca/tools/gpunetio_ib_write_lat/`, checking GPU-NIC pairing, reading the half-iter / full-iter / CUDA-side usec columns, characterizing median / p99 / jitter for a real-time control loop, picking GPUNetIO vs GPI vs CPU-initiated `perftest`, or weighing the latency-vs-batching trade-off. Trigger even without 'GPUNetIO' or 'ib_write_lat': 'GPU kernel RDMA latency benchmark', 'how fast can a CUDA kernel post a WRITE', 'p99 RDMA latency on H100 + ConnectX', 'kernel-launched WR tail latency', or 'compare GPU-init vs CPU-init perftest'. Route elsewhere for bandwidth runs (doca-gpunetio-ib-write-bw), the GPI surface (doca-gpi), library debugging (doca-gpunetio), or DOCA install.

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帮助编写和检查PyTorch模型与训练代码，涵盖数据加载、实验可复现性、训练状态保存与恢复，并提供改善GPU显存占用和运行速度的方法。

• 编写与审查PyTorch训练代码
• 排查实验结果不一致的原因
• 改善加载速度与GPU显存占用

检查已完成预订的Itô GPU环境是否可用于机器学习训练。目前不执行训练，而是说明尚未提供的功能，并整理未来交接所需的条件。

• 预订GPU后确认能否训练
• 查看微调任务的交接要求
• 整理预订、预算和确认要求

针对在Itô已预订GPU上进行模型服务的咨询，说明当前尚未支持的范围，并整理未来所需的配置、资格验证及确认事项；目前不会启动模型服务。

• 确认预订Itô GPU后的服务支持情况
• 了解OpenAI兼容端点的限制
• 查看未来模型服务所需配置

帮助在有限GPU显存下配置LoRA和QLoRA，对大语言模型进行微调，并支持保存训练得到的适配器、按用途切换适配器及将其合并到基础模型。

• 在有限显存下微调模型
• 选择LoRA训练设置
• 按用途切换适配器

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

中文简介正在准备中。当前显示英语简介。

• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Design and review desktop interfaces built with GPUI Kit. Applies rules for layouts, colors, components, interaction states, and interface wording, with checks for keyboard access, focus behavior, and accessibility before finishing the work.

中文简介正在准备中。当前显示英语简介。

• Designing a new GPUI Kit screen
• Reviewing colors, spacing, and components
• Checking dialog and keyboard behavior

Run TVM's TIRx compiler tests alongside tirx-kernels import and correctness checks. It verifies the local build, selects GPUs, runs parallel tests, saves full logs, and guides failure investigation and result reporting.

中文简介正在准备中。当前显示英语简介。

• Testing TIRx compiler and kernel changes
• Checking tirx-kernels imports and registry entries
• Running parallel tests across selected GPUs

Evaluate NVIDIA Cosmos Policy for robot manipulation in LIBERO and RoboCasa simulations. Follow GPU environment setup, configure rendering without a display, run small checks before larger benchmarks, inspect success rates, and profile inference latency.

中文简介正在准备中。当前显示英语简介。

• Running a small LIBERO evaluation
• Evaluating kitchen tasks in RoboCasa
• Setting up simulation on a GPU cluster without a display