NVIDIA Corporation

Start a cuOpt REST server and build Python or curl clients to submit routing and optimization problems. Follow result polling, translate Python API field names into REST fields, and diagnose rejected requests.

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• Start a cuOpt server locally or in Docker
• Submit a routing problem from Python
• Retrieve optimization results by polling

Build and troubleshoot a GPU-powered service that checks factory videos for missing or out-of-order procedure steps. Guides video segmentation, action classification with a vision-language model, deployment, evaluation, and latency measurement for files and live cameras.

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• Building a service to check assembly-line procedure compliance
• Testing video files, RTSP streams, and Basler cameras
• Investigating action classification and step-order errors

Build DeepStream commands for image and video inference, object detection, tracking, and streaming. The skill asks about missing requirements, assembles a GStreamer processing pipeline, and checks its syntax, components, properties, and connections where supported.

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• Build a pipeline for inference on local images or video
• Detect and track objects across multiple RTSP streams
• Save processed output to an MP4 file

Continue training a KERMT molecular model on a custom SMILES corpus, where text strings describe molecular structures. The skill validates data and checkpoints, prepares training inputs, and launches containerized training with saved logs and checkpoints.

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• Continue molecular model pretraining on a custom SMILES corpus
• Start additional training with a new learning-rate schedule
• Resume an interrupted training run from a saved checkpoint

Pretrain a new KERMT chemistry model from a molecular corpus, starting with random weights. The skill validates the data, builds fresh vocabularies, estimates runtime, and launches containerized training with logs and records for reproducing the run.

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• Train a new model on a specialized chemistry corpus
• Run pretraining without an existing checkpoint
• Choose a training objective and batch size

Build English clinical speech-recognition benchmarks by selecting medical terms, checking pronunciations, and generating synthetic audio. Outputs include audio files and a NeMo manifest, a structured file listing recordings and metadata, ready for a separate scoring stage.

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• Building a speech-recognition benchmark for a clinical specialty
• Checking synthesized pronunciations of drug and procedure names
• Generating evaluation audio across voices, noise, and contexts

Predict protein structures by connecting MSA-Search with OpenFold3. It finds related sequences, aligns them to provide evolutionary context, and saves predicted structures with confidence scores. It also supports comparing predictions with and without that context.

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• Predict protein structures using related sequences
• Save alignments, structures, and confidence scores
• Compare predictions with and without alignment context

Turn written planning problems into mathematical optimization models. Separate fixed inputs, constraints, decisions, and objectives, then choose linear, mixed-integer, or quadratic formulations for tasks such as production planning, inventory management, and material allocation.

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• Formulate production plans with capacity and cost constraints
• Model inventory limits across purchasing and sales periods
• Define multiple goals in priority order

Find related protein sequences and generate multiple sequence alignments, which line up corresponding positions across sequences. Use NVIDIA's hosted API or a local Docker service for standard searches, paired complex alignments, and local structural template searches.

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• Generate protein alignments in A3M or FASTA format
• Create paired alignments for protein complexes
• Search PDB70 for structural templates locally

Design protein backbones, the structural framework of proteins, with RFDiffusion through NVIDIA NIM. Generate new structures, build around existing structural motifs, or design binders, then save PDB files for subsequent amino acid sequence design.

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• Generating a new protein backbone
• Building a backbone around an existing structural motif
• Designing a binder around specified target residues

Predict structures of proteins, DNA, RNA, and complexes with small molecules using OpenFold3. Guides hosted API requests or local Docker setup, saves structure files, and helps interpret confidence scores supplied with the predictions.

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• Predicting protein structures through the hosted API
• Predicting complexes with DNA, RNA, or small molecules
• Setting up local inference with Docker

Turn molecules listed in a SMILES CSV into numerical feature vectors using a KERMT model. The workflow checks the model and input, cleans molecular notation, and saves four sets of vectors with validity information.

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• Extract molecular feature vectors from a SMILES CSV
• Reuse a local KERMT checkpoint
• Extract features with a released pretrained model

Evaluate speech recognition on synthetic clinical audio listed in a NeMo manifest. Produce four error metrics and a five-section report highlighting missed medical terms, then use pronunciation and noise breakdowns to guide dataset improvements or fine-tuning decisions.

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• Scoring medical term recognition on synthetic audio
• Generating an error report by term, category, and noise level
• Investigating accuracy gaps between pronunciation sources

Set up AutoMagicCalib from NGC release containers using Docker Compose. The skill handles registry authentication, port and path configuration, optional VGGT model download, service startup, and readiness checks for the backend and web interface.

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• Launching the AutoMagicCalib backend and web interface
• Configuring service ports and network access
• Adding the optional VGGT model during setup

Fine-tune Parakeet TDT v2 on clinical vocabulary using NeMo, then compare key-term error rates before and after training. The results guide whether to keep the model, expand the dataset, or prepare it for deployment.

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• Reducing transcription errors in medication names
• Checking whether training improved clinical term recognition
• Deciding whether to expand the training dataset

Fine-tune a pretrained KERMT model on a CSV of molecular structures and labeled targets. The workflow checks inputs, prepares features, launches training in a container, and saves trained models, logs, and test results.

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• Train a molecular model on labeled CSV data
• Select specific prediction targets for training
• Reuse prepared data to compare training settings

Predict how small molecules may bind to proteins using DiffDock through NVIDIA NIM. Prepare molecular inputs, run hosted API or local Docker requests, and save ranked binding poses with confidence scores for inspection.

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• Predicting small-molecule binding poses for a protein
• Submitting SMILES or SDF ligands
• Saving ranked poses with confidence scores

Install cuOpt for Python, C, or use as a REST server, a service called over HTTP. Choose pip, conda, or Docker to match the environment, verify installation, and troubleshoot package or CUDA mismatches.

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• Installing cuOpt for Python
• Setting up the C library and headers
• Launching and checking a REST server

Predict biomolecular structures and estimate how strongly a ligand binds using Boltz2 NIM. Submit proteins, DNA, RNA, and ligand inputs through NVIDIA's hosted API or local Docker, then save structures and inspect confidence and affinity scores.

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• Predicting protein structures and DNA or RNA complexes
• Providing ligand inputs with SMILES or CCD identifiers
• Estimating binding affinity for one ligand

Calibrate multiple cameras from recorded MP4 files through the AutoMagicCalib REST API. The skill handles project creation, uploads, input verification, progress checks, and evaluation when reference data is supplied, with optional VGGT refinement.

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• Calibrating cameras from synchronized MP4 recordings
• Uploading local settings and alignment files
• Evaluating calibration against ground-truth data

Convert a single CT DICOM series into a NIfTI volume with Hounsfield units, the CT intensity scale. A JSON summary records image dimensions, spacing, orientation, and the coordinate transformation matrix for checking the conversion.

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• Converting a single CT series into a 3D NIfTI volume
• Checking CT intensity ranges and voxel spacing
• Inspecting image orientation before segmentation

Generate molecular predictions from a CSV of SMILES strings, a text notation for molecular structures, using a finetuned KERMT model. Validate inputs, clean data, compute molecular features, and return predictions with execution logs and a replay command.

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• Predict targets for molecules listed in a SMILES CSV
• Check whether a saved model is suitable for inference
• Reuse prepared data for another prediction run

Modify NVIDIA cuOpt itself, covering C++/CUDA solver code, Python bindings, server endpoints, and CI. Follow project conventions for environment setup, builds, tests, debugging, and contributions, including the required sign-off for submitted changes.

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• Fixing solver internals and investigating numerical issues
• Changing Python APIs or server endpoints
• Setting up a development environment and running tests

Port quantum circuits from Qiskit or other frameworks into CUDA-Q while preserving the original algorithm. Guides gate conversion, measurement ordering, precision checks, and small test comparisons to produce code that runs without the source framework.

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• Porting Qiskit circuits to CUDA-Q
• Comparing measurement results after migration
• Removing source framework dependencies