NVIDIA Corporation

Set up Auto Ontology with Helm or Docker Compose, or connect an agent to an existing deployment through MCP, an agent connection protocol. Guides database connections, readiness checks, and troubleshooting for startup and sign-in problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Installing Auto Ontology on Kubernetes
• Running the stack on one machine with Docker Compose
• Connecting an agent to an existing deployment

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Edit existing RTX Remix projects for classic DirectX 8/9 games through an MCP connection to the Toolkit. Replace models and textures, manage scene layers, and save and verify changes to captured game assets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Replacing textures in a classic game's Remix project
• Swapping captured models for existing replacement assets
• Organizing edits in scene layers

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Benchmark a deployed NVIDIA RAG Blueprint server using a YAML configuration. Measure time spent in each processing stage, run optional aiperf load tests, and compare response latency, throughput, and bottlenecks across concurrency and retrieval settings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find slow stages in a running RAG server
• Compare performance across concurrency levels
• Measure the impact of retrieval and reranking settings

Optimize stock allocations from price data using NVIDIA cuOpt, with controls for severe losses or overall variability. Compare risk and return, inspect allocation tables, backtest against benchmarks, and evaluate scheduled or drift-triggered rebalancing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate allocations under risk and asset weight limits
• Compare allocations across risk preferences
• Backtest an allocation against an equal-weight portfolio

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Generates a standalone YOLO26 detection and tracking service scaffold for VSS Search and Alerts. Includes DeepStream configuration, Docker files, and tests, with Kafka integration guidance and clear limits on validation before model assets are available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating a standalone YOLO26 service for VSS
• Generating a scaffold before model assets are ready
• Checking Kafka integration with VSS workflows

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Evaluate object detection predictions against labeled ground truth with TAO Data Services. Produce class-level precision, recall, accuracy, and AP, a detection quality measure, with CSV results and precision–recall plots for reviewing model performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Score object detection predictions against ground truth
• Review precision and recall by class
• Generate metrics CSV files and precision–recall plots

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Find NVIDIA skills for a product or workflow using the current catalog. Get up to three relevant recommendations with reasons, installation commands, and starter prompts, or use an already installed skill when one fits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find skills for Jetson setup and troubleshooting
• Discover skills for CUDA or GPU development
• Identify skills for NeMo or NIM workflows

Investigate failed, stalled, or timed-out NVFLARE federated learning jobs, which coordinate training across sites. Collect limited logs and job artifacts, match known failure patterns, and report likely causes, confidence, and concrete recovery steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a failed local simulation
• Diagnosing stalled or timed-out jobs
• Checking client loss or EXECUTION_EXCEPTION errors

Create or refresh a governance card, a structured review document for an existing agent skill. It gathers local source evidence, renders a Markdown draft, and checks for unresolved review markers before submission for human review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a governance card for an existing skill
• Refresh a card after skill changes
• Prepare material for legal and safety review

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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