데이터·AI 스킬

Generate synthetic CT volumes with paired masks that mark anatomical regions or tumors. Configure body regions and output dimensions, then inspect image slices, label mappings, and recorded generation details for synthetic research workflows.

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• Generate synthetic chest CT volumes with lung masks
• Explore tumor size settings in synthetic CT generation
• Preview configuration and resource estimates before generation

Fine-tune NV-Segment-CT VISTA3D on CT images and region labels in NIfTI format. Choose standard training or fixed-class softmax training, save model checkpoints and evaluation records, and optionally track training with MLflow.

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• Fine-tuning with paired CT images and region labels
• Checking the training workflow with a smoke run
• Training mutually exclusive classes with softmax

Turn records from a stopped NVFLARE Auto-FL optimization campaign into a reproducible Markdown report and JSON summary. Summarize improvements, retained candidates, failed ideas, and literature outcomes, with a refreshed progress plot when plotting dependencies are available.

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• Create a final report after an optimization campaign stops
• Summarize baseline scores and retained improvements
• Review successful and unsuccessful literature-based ideas

Replace the object detector in VSS Alerts Blueprint verification mode with an ONNX model. This guide covers model placement, interpreting detection outputs, DeepStream configuration, TensorRT engine builds, redeployment, and common loading or mount failures.

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• Replace the verification detector with a YOLO11 ONNX model
• Investigate DeepStream custom parser loading failures
• Fix ONNX mounts and TensorRT engine rebuild settings

Find the right NVIDIA NuRec skills for turning sensor recordings into renderable 3D scenes. This routing guide organizes dataset downloads, conversion, training, rendering, and frame cleanup into workflows handled by separate specialist skills.

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• Choose the right NuRec skill
• Plan 3D reconstruction from sensor recordings
• Render a pretrained NuRec scene

Run video augmentation, which creates variations of source videos, and automatic labeling on OSMO. Select a workflow, check credentials and resources, monitor execution, download outputs, and compare augmented videos with their originals.

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• Automatically label source videos
• Create video variations and label the outputs
• Process original and augmented videos in one workflow

Segment specified organs in CT NIfTI scans using NV-Segment-CT VISTA3D. Produces a label map identifying organ regions and JSON evidence covering labels, voxel counts, physical volumes, geometry checks, and model identity for engineering validation.

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• Segmenting the liver, spleen, and both kidneys in CT scans
• Recording voxel counts and physical volumes for each organ
• Checking output geometry and unexpected labels

Find PhysicsNeMo models, data loading pipelines, and examples for machine learning tasks involving physical systems. The skill checks current repository files, presents relevant alternatives, and suggests a reading order without writing training code.

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• Finding model families for physical forecasting
• Comparing models for grids and unstructured meshes
• Finding data loading pipelines for a dataset

Helps configure training across multiple GPUs or machines in NeMo AutoModel. Guides strategy selection, YAML settings, and parallelism choices that split model computation or data, with constraints for large models, long sequences, and expert models.

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• Choosing a strategy for multi-GPU training
• Configuring parallelism for large models
• Splitting long input sequences across GPUs

Search document collections with the NeMo Retriever CLI. Ingest files into a local search index or use a deployed service, then retrieve evidence for questions across PDFs, Office files, images, audio, and video.

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• Find evidence across internal documents
• Build a local search index from files
• Query an existing Retriever service

Plan and troubleshoot Nemotron retrieval recipes for embeddings, which turn text into searchable vectors, and reranking, which reorders search results. Review configurations, prepare training and evaluation commands, and check export or deployment requirements.

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• Choosing training steps for missing relevant documents
• Improving the ordering of retrieved passages
• Reviewing training commands with a dry run

Build or repair NVIDIA voice agents with Pipecat or LiveKit for real-time spoken conversations. Set up speech recognition, language models, and speech synthesis, customize pronunciation or multilingual routing, and deploy locally or in the cloud.

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• Build a new real-time voice agent
• Repair an existing voice conversation pipeline
• Customize speech recognition and pronunciation

Search documents through NeMo Retriever MCP and use retrieved passages to support answers. Add missing documents when server access is confirmed, then search them while preserving available document identifiers and page numbers for citations.

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• Finding evidence in indexed documents
• Adding missing documents and searching their contents
• Preparing answers with document identifiers and page references

Plan and tune training for vision-language models that use MoE, a mixture of expert networks, in Megatron Bridge. Compares FSDP and 3D parallel training, with guidance on memory use, batch sizes, and performance checks using realistic image inputs.

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• Choosing a parallel training approach for a new model
• Investigating training failures and out-of-memory errors
• Comparing performance with realistic image inputs

Create and modify NeMo AutoModel training and evaluation recipes. Get guidance on YAML configuration, functions that build training components, execution flow, validation, and checkpoint saving and resuming, with minimal examples and local checks.

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• Create a new fine-tuning recipe
• Add or change YAML configuration fields
• Configure validation, checkpoint saving, and resuming

Guide repeated NeMo-RL or NeMo-gym experiments to test hypotheses and improve training settings. Establish a baseline, compare relevant metrics, and preserve reproducible records in git and TSV logs while keeping users involved in launch decisions.

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• Testing hypotheses about training settings
• Comparing accuracy, reward, or throughput
• Recording reproducible experiment histories

Configure and validate communication overlap in Megatron-Bridge for MoE models, which distribute work among expert networks. Check compatibility, compare training speed, and investigate regressions across standard dispatch, DeepEP, HybridEP, and delayed weight-gradient computation.

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• Enable expert communication overlap in MoE training
• Check compatibility before using DeepEP or HybridEP
• Compare plain overlap and delayed weight-gradient computation

Configure CPU offloading in Megatron Bridge to reduce GPU memory use by moving intermediate training data or optimizer states to CPU memory. Check compatibility, adjust the offloaded fraction, and investigate memory errors or slowdowns.

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• Reduce GPU memory use during model training
• Tune how much optimizer state moves to CPU memory
• Check compatibility with parallelism and CUDA graphs

Find starting configurations for training MoE models, which route work among specialized expert networks, on H100, B200, GB200, and GB300. Provides model-specific parallel layouts, communication options, and approximate performance bands to guide measured comparisons.

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• Choose initial MoE training settings for specific hardware
• Compare communication backends and parallel layouts
• Estimate approximate training throughput for planning

Plan long-sequence training for MoE models, which divide work among expert networks, in Megatron Bridge. Use experiment-based guidance to balance parallelism, selective recalculation, and routing choices when investigating memory shortages or reduced training throughput.

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• Planning long-sequence MoE training configurations
• Investigating memory shortages during long-context training
• Reviewing throughput drops after a code change

Configure Megatron-Bridge to overlap communication with computation during distributed training. Review settings for tensor, data, and pipeline parallelism, check activation requirements, and use included tests to investigate configuration issues or throughput regressions.

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• Enable communication overlap in distributed training
• Check why tensor parallel overlap is inactive
• Investigate slower training after configuration changes

Choose how to distribute tokens, the units a model processes, across MoE model experts during training. The guide compares alltoall, DeepEP, and HybridEP by GPU hardware, parallelism scale, memory needs, and optimization stage.

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• Choosing a MoE dispatcher for specific GPU hardware
• Establishing an alltoall baseline for initial setup
• Comparing DeepEP and HybridEP under matching conditions

Configure and validate sequence packing, which combines training examples into token sequences, and long-context training in Megatron-Bridge. Compare offline, in-batch, and Energon online approaches while checking batch sizes, parallelism requirements, and model compatibility.

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• Configure offline packing for text model fine-tuning
• Choose packing methods for vision-language models
• Check parallelism constraints for long-context training

Choose Megatron Bridge training configurations for a model, GPU allocation, and training goal. Get starting commands and parallel processing adjustments, with clear distinctions between changes that affect learning behavior, execution tuning, and benchmark-only settings.

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• Choose a starting configuration for pretraining or fine-tuning
• Adapt parallel processing settings to available GPUs
• Compare training and throughput benchmark configurations