数据与 AI技能

Run NV-Reason-CT on a supplied 3D chest or abdominal CT scan and a text prompt for research or engineering. Returns generated text and JSON records of input geometry, model identity, dependencies, and limitations.

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• Testing inference on a chest CT scan
• Running abdominal CT inference with a focused question
• Checking local model assets and runtime readiness

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

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

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

Fine-tune the MAISI VAE, a model that compresses and reconstructs CT or MRI volumes, using NIfTI datasets. Prepare training settings, check inputs before training, and save model checkpoints and loss histories for review.

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• Check a NIfTI dataset before GPU training
• Fine-tune a VAE with CT or MRI volumes
• Compare reconstruction losses across training runs

Investigate failures in NVIDIA TAO Visual ChangeNet classification experiments using prediction scores and actual images. Produces an illustrated root cause report covering data, reference images, training settings, and simulated changes to assess possible fixes.

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• Investigate missed defects and poor recall
• Analyze FAR regressions and false positives
• Audit reference images and inspection data

Optimize an existing NVFLARE federated learning job, which trains across participating sites, through isolated candidate changes. Compare results under consistent training budgets and metric definitions, while preserving the best source and recording changes, scores, and artifacts.

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• Compare changes to an existing federated learning job
• Evaluate two approaches under a fixed training budget
• Resume optimization using recorded candidates and results

Generate synthetic body MRI volumes, or three-dimensional images, with NV-Generate-CTMR rflow-mr. The skill stages configuration settings, runs the existing inference workflow, and records output geometry and data checks alongside the generated images.

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• Generate synthetic body MRI volumes for experimentation
• Try supported MRI modalities such as T1 and T2
• Compare outputs with different settings and random seeds

Export existing medical AI inference results to MLflow, a system for recording machine learning runs. Preview sanitized metadata, parameters, reproducibility details, and engineering quality metrics before export, with optional image artifacts under explicit upload policies.

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• Recording completed inference runs in MLflow
• Keeping generation parameters and reproducibility details
• Previewing export contents without contacting MLflow

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

Configure fault detection and recovery for distributed training in Megatron Bridge. The skill covers slow GPU detection, automatic restarts, job interruption handling, and checkpoint saving, with configuration examples and guidance for investigating hangs or failed recovery.

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• Configuring automatic recovery after training failures
• Detecting slow GPUs in distributed training
• Preparing for scheduled job interruptions

Helps tune activation recomputation in Megatron Bridge, recalculating intermediate training values to reduce GPU memory use. Guides selective or full-layer settings, checks compatibility, and compares memory peaks with training speed to assess whether a configuration completes training steps.

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• Investigate training failures caused by activation memory
• Choose recomputation targets for attention or MoE models
• Compare GPU memory savings against training speed

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

Enable Megatron FSDP in Megatron-Bridge to distribute model training state across GPUs. Follow configuration and checkpoint requirements, investigate memory failures or performance regressions, and verify the setup with an existing two-GPU functional test.

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• Switch from DDP to Megatron FSDP
• Investigate memory failures after FSDP configuration changes
• Check checkpoint formats and incompatible settings

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.

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

Improve MoE training throughput in Megatron Bridge. MoE models divide work among expert networks; this skill identifies memory, communication, and compute bottlenecks, guides controlled configuration comparisons, and validates speed alongside training health.

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• Investigate training slowdowns after code or configuration changes
• Find memory, communication, and compute bottlenecks
• Compare parallelism, precision, and dispatcher settings

Plan domain or language adaptation for Nemotron Speech recognition. Compare word boosting, language models, and fine-tuning based on available audio, accuracy targets, and budget, then route data preparation, training, evaluation, and deployment to the appropriate sub-skills.

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• Choose an approach for recognizing specialist terminology
• Plan adaptation for accents, noise, or another language
• Estimate audio data, GPU, time, and cost requirements

Try NeMo Relay through a CLI wrapper, a supported integration, or direct Python, Node.js, or Rust instrumentation. Choose a minimal first run and verify recorded tool and model activity before considering one additional plugin.

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• Try Relay with Codex or Claude Code through the CLI
• Choose a supported integration for an existing application
• Check recorded tool and LLM calls in an application

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 NeMo AutoModel training launches on a local node, a Slurm cluster scheduler, or cloud resources through SkyPilot. Prepare launch settings for GPUs, containers, environment variables, multi-node communication, and optional performance profiling.

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• Switching between local and cluster training launches
• Configuring a multi-node Slurm training job
• Preparing SkyPilot spot jobs with checkpoint recovery

Plan and configure Nemotron customization workflows using existing steps for data preparation, translation, training, conversion, optimization, and evaluation. Check how outputs connect between stages and produce configuration files suited to the supplied models, data, and hardware.

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• Configure LoRA fine-tuning for a supplied model and dataset
• Connect data preparation and supervised fine-tuning
• Benchmark an existing hosted model endpoint

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

Add support for new model architectures in NeMo AutoModel, from inspecting configuration files to implementing layers, converting saved weights, registering models, and testing numerical agreement with Hugging Face implementations.

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• Adding support for a new language model
• Mapping MoE router and expert weights
• Integrating a model that handles images and text

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