All Agent Skills
41,263 results
- Compute Sanitizer Diagnose Cuda BugsNVIDIAOfficialDetect and diagnose CUDA correctness bugs with NVIDIA Compute Sanitizer. Routes GPU memory errors, shared-memory data races, uninitialized-memory reads, and synchronization/barrier bugs to the right sub-tool (memcheck, racecheck, initcheck, synccheck) and explains how to build, run, and read the report.
- CUDA-Q GuideNVIDIAOfficialUse for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.
- CUDA-Q ImportingNVIDIAOfficialUse when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.
- CufolioNVIDIAOfficialUse when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, efficient frontiers, scenario generation, or NVIDIA cuOpt.
- Cuopt DeveloperNVIDIAOfficialModify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.
- Cuopt InstallNVIDIAOfficialInstall cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.
- Cuopt Multi Objective ExplorationNVIDIAOfficialTrace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).
- Cuopt Numerical Optimization APINVIDIAOfficialLP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
- Cuopt Numerical Optimization API CNVIDIAOfficialLP, MILP, and QP (beta) with cuOpt — C API only. Use when the user is embedding LP, MILP, or QP in C/C++.
- Cuopt Numerical Optimization API CliNVIDIAOfficialLP, MILP, and QP (beta) with cuOpt — CLI only (MPS/LP/QPS files, cuopt_cli). Use when the user is solving LP, MILP, or QP from an MPS or LP file via command line.
- Cuopt Numerical Optimization API PythonNVIDIAOfficialSolve LP, MILP, QP (beta) with cuOpt Python API — linear/quadratic objectives, integer variables, scheduling, portfolio, least squares.
- Cuopt Numerical Optimization FormulationNVIDIAOfficialLP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.
- Cuopt Routing API PythonNVIDIAOfficialVehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
- Cuopt Routing FormulationNVIDIAOfficialVehicle routing (VRP, TSP, PDP) — problem types and data requirements. Domain concepts; no API or interface.
- Cuopt Server API PythonNVIDIAOfficialcuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
- Cuopt Server CommonNVIDIAOfficialcuOpt REST server — what it does and how requests flow. Domain concepts; no deploy or client code.
- Cuopt Skill EvolutionNVIDIAOfficialAfter solving a non-trivial problem, detect generalizable learnings and propose skill updates. Always active — applies to every interaction.
- Cupynumeric Hdf5NVIDIAOfficialRead and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk…
- Cupynumeric InstallNVIDIAOfficialInstall and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.
- Cupynumeric Migration ReadinessNVIDIAOfficialPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning.
- Cupynumeric Parallel Data LoadNVIDIAOfficialLoad a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.
- Dali Dynamic ModeNVIDIAOfficialDALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.
- Data DesignerNVIDIAOfficialUse when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
- Deepstream DevNVIDIAOfficialNVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.