数据与 AI技能

Prepare or run variant scoring with public CodonFM Encodon models. Validate coding sequences and codon positions in CSV inputs, assemble the scoring command, and check output arrays that compare reference and alternate codon probabilities in context.

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• Validating a variant CSV before scoring
• Preparing an Encodon scoring command
• Running scoring and checking output arrays

Prepare a public CodonFM v1 environment and download supported Encodon checkpoints, the files containing trained model weights. Covers container or host setup, GPU access checks, and verification of downloaded weights and configuration files.

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• Prepare a CodonFM development container
• Set up CodonFM directly on the host
• Download public Encodon model weights

Validate coding-sequence CSV files and extract embeddings, numerical features representing each sequence, with public CodonFM/Encodon models. Prepare extraction commands, choose suitable checkpoints, and verify outputs for downstream modeling of translation efficiency, expression, or mRNA stability.

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• Validate coding-sequence CSV files before extraction
• Extract numerical sequence features with Encodon
• Choose a checkpoint for downstream property modeling

Guides fine-tuning public Encodon models on labeled protein-coding sequences or variants. Supports numerical prediction and classification, with data checks, LoRA adapters that train selected parameters, head-only training, or full training, followed by checkpoint and metric checks.

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• Fine-tuning Encodon for sequence-level numerical prediction
• Training a classifier on labeled coding variants
• Preparing training data and a launch command

Convert existing PyTorch training code into an NVFLARE federated learning job, where sites train with their own data. Adapt training and evaluation, exchange model updates through the Client API, validate locally, and export the job.

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• Convert a manual PyTorch training loop to federated learning
• Adapt existing evaluation code for federated rounds
• Validate and export a FedAvg training job

Generate synthetic 3D brain MRI volumes with NV-Generate-CTMR MR-Brain v1, choosing scan types such as T1 or FLAIR. The wrapper records generation settings and output checks for experimental review, with restrictions on clinical and production use.

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• Generating synthetic T1 or T2 brain MRI volumes
• Trying FLAIR or SWI generation settings
• Specifying a random seed for generation

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

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

Coordinate defect image generation, augmentation, and labeling for circuit board, metal surface, and glass inspection on OSMO. Select workflows that create initial board datasets from CAD scenes, process real images, or fine-tune NVIDIA Cosmos AnomalyGen models.

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• Creating initial circuit board inspection datasets from CAD scenes
• Generating circuit board texture and component pose defects
• Running inference and labeling on real inspection images

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

Convert existing PyTorch Lightning training code into an NVFLARE job for federated learning, where sites train collaboratively while keeping data local. It integrates model exchange through the Lightning Client API and supports local validation or job export.

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• Converting Lightning training code to federated learning
• Preparing collaborative training with data kept at each site
• Checking a converted job in a local simulation

Compute statistics across distributed NVFLARE sites without sharing raw data. Generate and validate jobs for numeric tables or image datasets, then report site-level and global summaries, missing data rates, and applied privacy settings.

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• Comparing numeric data distributions across sites
• Computing means, variances, and quantiles across sites
• Summarizing image counts and pixel intensities

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

Convert existing Hugging Face Trainer or TRL SFTTrainer code into an NVFLARE job for federated learning, where multiple sites train a shared model. Preserve training settings, validate locally, and export the resulting job.

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• Converting Trainer-based training into a federated job
• Adapting PEFT or LoRA fine-tuning for federated learning
• Preserving evaluation metrics during conversion

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

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

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

Provides shared guidance and templates used by NVFLARE conversion skills. Supports consistent dependency setup, site-specific data paths, validation, and custom model aggregation, combining results from participating sites. It is an internal companion rather than a standalone converter.

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• Consulting dependency setup rules during conversion
• Checking data paths for participating sites
• Following shared validation and reporting guidance

Deploy and manage NVIDIA RAG Blueprint, a system that retrieves information to support AI answers. Check the running environment, configure models and search features, troubleshoot unhealthy services, and verify affected workflows after changes.

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• Deploy NVIDIA RAG Blueprint
• Configure search and document ingestion
• Change models or enable image features

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

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

Run NV-Segment-CTMR to divide CT or MRI NIfTI volumes into labeled regions. Save the label map and records of region volumes, processing time, and geometry checks for technical verification, without clinical interpretation.

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• Automatically segmenting CT body volumes
• Segmenting body MRI or preprocessed brain MRI
• Requesting regions by specific label IDs

Evaluate answer quality in NVIDIA RAG Blueprint using local documents and question data. The skill guides dataset preparation, RAGAS scoring, configuration comparisons, and error diagnosis for RAG systems that retrieve documents to support generated answers.

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• Prepare documents and questions for evaluation
• Compare quality across retrieval and generation settings
• Interpret RAGAS result files