NVIDIA Corporation

Build and solve routing problems in Python with cuOpt, including delivery fleets, round trips, and pickup-delivery tasks. Define vehicle capacities, arrival windows, and visit order constraints, then inspect routes, total cost, and orders that cannot be served.

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• Calculate delivery routes with vehicle capacity limits
• Plan visits within specified arrival windows
• Define pickup-delivery pairs and visit order

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

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

Calibrate live cameras by recording their RTSP video streams through VIOS and processing the clips with AutoMagicCalib. The workflow imports settings and alignment files, checks readiness, tracks calibration progress, and retrieves results.

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• Calibrate cameras from live RTSP streams
• Import settings and alignment files for a camera scene
• Retrieve evaluation statistics using ground truth data

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

Integrate a supported object detection model from HuggingFace or NVIDIA NGC into NVIDIA DeepStream. Automates model acquisition and conversion, TensorRT engine building, detection output parsing, multi-stream performance benchmarks, and reports with charts in Markdown, HTML, and PDF.

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• Run the default RT-DETR model in DeepStream
• Integrate a supported HuggingFace or NVIDIA NGC detection model
• Compare performance across multiple video streams

Extend an existing grover_base model with a new decoder that reconstructs molecular structure strings, then continue pretraining on your corpus. The workflow validates inputs, rebuilds vocabulary components, and records the conversion and training outputs.

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• Extend an existing grover_base model with cMIM
• Continue pretraining on a molecular corpus
• Trace checkpoint conversion and training records

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

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

Test a running AMC service with the bundled four-camera warehouse dataset. Run camera calibration from upload to completion, then check distance and image projection errors against ground-truth data to verify the installation.

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• Verify a newly installed AMC service
• Check the sample's calibration errors against ground truth
• Walk through the sample workflow in Swagger UI

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

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 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

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

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

Generate drug-like molecule candidates with GenMol NIM through NVIDIA's hosted API or local Docker deployment. Expand molecular scaffolds, modify existing candidates, rank results by drug-likeness or lipophilicity, and save the ranked molecules to a file.

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• Generate new molecule candidates from scratch
• Add structures around a molecular scaffold
• Modify fragments of a lead molecule

Monitor KERMT jobs running in the background by reading run records, Docker container status, and logs. Summarize training progress and validation metrics, stream logs, or report final test metrics when a finetune run completes successfully.

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• Check progress on a background pretraining run
• Watch training logs and validation metrics live
• Review test metrics after finetuning completes

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

Prepare the KERMT environment by checking Docker and GPU access, building the local container image when needed, and testing whether PyTorch detects GPUs inside it. Report the image details and detected GPU count before other KERMT workflows begin.

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• Setting up KERMT on a new machine
• Checking GPU access inside a container
• Building the KERMT image for the first time

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

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

Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.

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Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.

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