[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-cudaq-guide":3,"mdc-hncfnl-key":31,"related-repo-nvidia-cudaq-guide":2077,"related-org-nvidia-cudaq-guide":2175},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":20,"repoUrl":21,"updatedAt":22,"license":23,"forks":24,"topics":25,"repo":26,"sourceUrl":29,"mdContent":30},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,14,17],{"name":9,"slug":8,"type":13},"tag",{"name":15,"slug":16,"type":13},"Simulation","simulation",{"name":18,"slug":19,"type":13},"Quantum Computing","quantum-computing",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:26:58.898253","Apache-2.0",281,[],{"repoUrl":21,"stars":20,"forks":24,"topics":27,"description":28},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fcudaq-guide","---\nname: \"cudaq-guide\"\ntitle: \"Cuda Quantum\"\ndescription: \"CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.\"\nversion: \"1.0.1\"\nauthor: \"CUDA-Q Team \u003Ccuda-quantum@nvidia.com>\"\ntags: [cuda-quantum, quantum-computing, onboarding, getting-started, nvidia]\ntools: [Read, Glob, Grep]\nlicense: \"Apache-2.0\"\ncompatibility: \"Python 3.10+, C++ 20\"\nmetadata:\n    author: \"CUDA-Q Team \u003Ccuda-quantum@nvidia.com>\"\n    tags:\n        - cuda-quantum\n        - quantum-computing\n        - onboarding\n        - getting-started\n        - nvidia\n    languages:\n        - python\n        - c++\n    domain: \"quantum\"\n---\n\n## CUDA-Q Getting Started Guide\n\nYou are a CUDA-Q expert assistant. Use `$ARGUMENTS` with the routing table\nbelow to jump straight to the topic the user needs.\n\n## Purpose\n\nGuide users through the CUDA-Q platform: installation, writing quantum kernels,\nGPU-accelerated simulation, connecting to QPU hardware, and exploring built-in\napplications.\n\n## Prerequisites\n\n- Python 3.10+ (for Python installation path)\n- CUDA Toolkit (for GPU-accelerated targets on Linux; not required on macOS)\n- NVIDIA GPU (optional; CPU-only simulation available via `qpp-cpu`)\n- For C++ path: Linux or WSL on Windows\n- For QPU access: provider-specific credentials and account\n\n## Instructions\n\n- Invoke with `\u002Fcudaq-guide [argument]`\n- If no argument is given, display the full onboarding menu and ask what\n  the user wants to explore\n- Pass an argument from the routing table below to jump directly to that topic\n- Read local CUDA-Q documentation files to answer questions accurately\n\n## References\n\n| Section | Doc file |\n| --- | --- |\n| Install | `docs\u002Fsphinx\u002Fusing\u002Finstall\u002Finstall.rst`, `docs\u002Fsphinx\u002Fusing\u002Fquick_start.rst` |\n| Test Program | `docs\u002Fsphinx\u002Fusing\u002Fbasics\u002Fkernel_intro.rst`, `docs\u002Fsphinx\u002Fusing\u002Fbasics\u002Fbuild_kernel.rst` |\n| GPU Simulation | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fsims\u002Fsvsims.rst`, `docs\u002Fsphinx\u002Fusing\u002Fexamples\u002Fmulti_gpu_workflows.rst` |\n| QPU | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware.rst`, `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fcloud.rst` |\n| Applications | `docs\u002Fsphinx\u002Fusing\u002Fapplications.rst` |\n| Parallelize | `docs\u002Fsphinx\u002Fusing\u002Fexamples\u002Fmulti_gpu_workflows.rst` |\n\n## Routing by Argument\n\n| Argument | Action |\n|---|---|\n| `install` | Walk through installation (see Install section) |\n| `test-program` | Build and run a Bell state kernel to verify CUDA-Q is working properly |\n| `gpu-sim` | Explain GPU-accelerated simulation targets (see GPU Simulation section) |\n| `qpu` | Explain how to run on real QPU hardware (see QPU section) |\n| `applications` | Showcase what can be built with CUDA-Q (see Applications section) |\n| `parallelize` | Show how to run circuits in parallel across multiple QPUs (see Parallelize section) |\n| _(none)_ | Print the full menu below and ask what they'd like to explore |\n\n---\n\n## Full Menu (no argument)\n\nPresent this when invoked with no argument\n\n```text\nCUDA-Q Getting Started\n\nCUDA-Q is NVIDIA's unified quantum-classical programming model for CPUs, GPUs, and QPUs.\nSupports Python and C++. Docs https:\u002F\u002Fnvidia.github.io\u002Fcuda-quantum\u002F\n\nChoose a topic\n  \u002Fcudaq-guide install         Install CUDA-Q (Python pip or C++ binary)\n  \u002Fcudaq-guide test-program    Write and run your quantum kernel\n  \u002Fcudaq-guide gpu-sim         Accelerate simulation on NVIDIA GPUs\n  \u002Fcudaq-guide qpu             Connect to real QPU hardware\n  \u002Fcudaq-guide applications    Explore what you can build\n  \u002Fcudaq-guide parallelize     Run circuits in parallel across multiple QPUs\n```\n\n---\n\n## Install\n\nInstructions\n\n- Default to Python installation unless the user explicitly mentions C++ or\n  the `nvq++` compiler.\n- After installation, always guide the user through the validation step\n  (run the Bell state example and confirm output shows `{ 00:~500 11:~500 }`).\n- Default to GPU-accelerated targets (`nvidia`) unless: the user is on\n  macOS\u002FApple Silicon, mentions no GPU available, or explicitly asks for\n  CPU-only simulation - in those cases use `qpp-cpu`.\n- Do not suggest cloud trial or Launchpad options unless the user has no\n  local environment or asks about cloud access.\n\nPlatform notes\n\n- Linux (x86_64, ARM64): full GPU support -\n  `pip install cudaq` + CUDA Toolkit\n- macOS (ARM64\u002FApple Silicon): CPU simulation only -\n  `pip install cudaq` (no CUDA Toolkit needed)\n- Windows: use WSL, then follow Linux instructions\n- C++ (no sudo):\n  `bash install_cuda_quantum*.$(uname -m) --accept -- --installpath $HOME\u002F.cudaq`\n- Brev (cloud, no local setup): Log in at the NVIDIA Application Hub,\n  open a CUDA-Q workspace, then SSH in with the Brev CLI:\n\n  ```bash\n  brev open ${WORKSPACE_NAME}\n  ```\n\n  CUDA-Q and the CUDA Toolkit are pre-installed.\n\n---\n\n## Test Program\n\nKey concepts to explain\n\n- `@cudaq.kernel` \u002F `__qpu__` marks a quantum kernel - compiled to Quake MLIR\n- `cudaq.qvector(N)` allocates N qubits in |0⟩\n- `cudaq.sample()` - kernel measures qubits; returns bitstring histogram\n  (`SampleResult`)\n- `cudaq.run()` - kernel returns a classical value; runs `shots_count` times\n  and returns a list of those return values\n- `cudaq.observe()` - computes expectation value ⟨H⟩ for a spin operator\n- `cudaq.get_state()` - returns the full statevector (simulator only)\n\nKernel restrictions\n\n- Only a restricted Python subset is valid inside a kernel - it compiles to\n  Quake MLIR, not regular Python.\n- NumPy and SciPy cannot be used inside a kernel. Use them outside the kernel\n  for classical pre\u002Fpost-processing.\n- Kernels can call other kernels; the callee must also be a `@cudaq.kernel`.\n\nFor compiler internals (`inspect` module -> `ast_bridge.py` -> Quake MLIR ->\nQIR -> JIT), route to `\u002Fcudaq-compiler`.\n\n---\n\n## GPU Simulation\n\nTo recommend the best simulation backend for the user, consult the full\ncomparison table at\n\u003Chttps:\u002F\u002Fnvidia.github.io\u002Fcuda-quantum\u002Flatest\u002Fusing\u002Fbackends\u002Fsimulators.html>\n\n### Available GPU Targets\n\n| Target | Description | Use when |\n|---|---|---|\n| `nvidia` (default) | Single-GPU state vector via cuStateVec (up to ~30 qubits) | Default choice for most simulations on a single GPU |\n| `nvidia --target-option fp64` | Double-precision single GPU | Higher numerical precision needed (e.g. chemistry, sensitive observables) |\n| `nvidia --target-option mgpu` | Multi-GPU, pools memory across GPUs (>30 qubits) | Circuit exceeds single-GPU memory; requires MPI |\n| `nvidia --target-option mqpu` | Multi-QPU, one virtual QPU per GPU, parallel execution | Running many independent circuits in parallel (e.g. parameter sweeps, VQE gradients) |\n| `tensornet` | Tensor network simulator | Shallow or low-entanglement circuits; qubit count exceeds statevector feasibility |\n| `qpp-cpu` | CPU-only fallback (OpenMP) | No GPU available; macOS; small circuits for testing |\n\n---\n\n## QPU\n\nWhen the user invokes this section, do not dump all providers at once.\nInstead, follow this two-step dialogue:\n\nStep 1 - ask which technology they want\n\n```text\nWhich QPU technology are you targeting?\n  1. Ion trap       (IonQ, Quantinuum)\n  2. Superconducting (IQM, OQC, Anyon, TII, QCI)\n  3. Neutral atom   (QuEra, Infleqtion, Pasqal)\n  4. Cloud \u002F multi-platform (AWS Braket, Scaleway)\n```\n\nStep 2 - once they pick a technology, ask which provider, then read the\ncorresponding doc file and walk the user through it step by step.\n\n| Technology | Provider | Doc file |\n|---|---|---|\n| Ion trap | IonQ | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fiontrap.rst` (IonQ section) |\n| Ion trap | Quantinuum | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fiontrap.rst` (Quantinuum section) |\n| Superconducting | IQM | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fsuperconducting.rst` (IQM section) |\n| Superconducting | OQC | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fsuperconducting.rst` (OQC section) |\n| Superconducting | Anyon | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fsuperconducting.rst` (Anyon section) |\n| Superconducting | TII | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fsuperconducting.rst` (TII section) |\n| Superconducting | QCI | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fsuperconducting.rst` (QCI section) |\n| Neutral atom | Infleqtion | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fneutralatom.rst` (Infleqtion section) |\n| Neutral atom | QuEra | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fneutralatom.rst` (QuEra section) |\n| Neutral atom | Pasqal | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fhardware\u002Fneutralatom.rst` (Pasqal section) |\n| Cloud | AWS Braket | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fcloud\u002Fbraket.rst` |\n| Cloud | Scaleway | `docs\u002Fsphinx\u002Fusing\u002Fbackends\u002Fcloud\u002Fscaleway.rst` |\n\nAfter walking through the provider steps, always close with\n\n- Test locally first with `emulate=True` before submitting to real hardware.\n- Use `cudaq.sample_async()` \u002F `cudaq.observe_async()` for non-blocking submission.\n- Handle provider credentials securely: export them as environment variables\n  in your shell session (or a local profile that is not committed to version\n  control) rather than hardcoding them in source or notebooks. Never paste\n  tokens into shared files, logs, or commits, and prefer a secrets manager\n  where one is available.\n\n---\n\n## Applications\n\nCUDA-Q ships with ready-to-run application notebooks\n\n| Category | Examples |\n|---|---|\n| Optimization | QAOA, ADAPT-QAOA, MaxCut |\n| Chemistry | VQE, UCCSD, ADAPT-VQE |\n| Error Correction | Surface codes, QEC memory |\n| Algorithms | Grover's, Shor's, QFT, Deutsch-Jozsa, HHL |\n| ML | Quantum neural networks, kernel methods |\n| Simulation | Hamiltonian dynamics, Trotter evolution |\n| Finance | Portfolio optimization, Monte Carlo |\n\n---\n\n## Parallelize\n\nCUDA-Q supports two distinct multi-GPU parallelization strategies - pick based\non what you are trying to scale.\n\n| Goal | Strategy | Target option |\n|---|---|---|\n| Single circuit too large for one GPU | Pool GPU memory | `nvidia --target-option mgpu` |\n| Many independent circuits at once | Run circuits in parallel | `nvidia --target-option mqpu` |\n| Large Hamiltonian expectation value | Distribute terms across GPUs | `mqpu` + `execution=cudaq.parallel.thread` |\n\n### Circuit batching with mqpu (`sample_async` \u002F `observe_async`)\n\nThe `mqpu` option maps one virtual QPU to each GPU. Dispatch circuits\nasynchronously with `qpu_id` to all GPUs simultaneously.\n\n```python\nimport cudaq\n\ncudaq.set_target(\"nvidia\", option=\"mqpu\")\nn_qpus = cudaq.get_platform().num_qpus()\n\nfutures = [\n    cudaq.observe_async(kernel, hamiltonian, params, qpu_id=i % n_qpus)\n    for i, params in enumerate(param_sets)\n]\nresults = [f.get().expectation() for f in futures]\n```\n\n### Hamiltonian batching\n\nFor a single kernel with a large Hamiltonian, add `execution=` to\n`cudaq.observe` — no other code change needed.\n\n```python\n# Single node, multiple GPUs\nresult = cudaq.observe(kernel, hamiltonian, *args,\n                       execution=cudaq.parallel.thread)\n\n# Multi-node via MPI\nresult = cudaq.observe(kernel, hamiltonian, *args,\n                       execution=cudaq.parallel.mpi)\n```\n\nSee the docs above for complete working examples of both patterns.\n\n---\n\n## Examples\n\n- `\u002Fcudaq-guide` — print the onboarding menu and ask the user which topic to\n  explore.\n- `\u002Fcudaq-guide install` — walk through installation, defaulting to the Python\n  `pip install cudaq` path, then validate with the Bell state example.\n- `\u002Fcudaq-guide test-program` — build and run a Bell state kernel and confirm\n  the output shows roughly `{ 00:~500 11:~500 }`.\n- `\u002Fcudaq-guide gpu-sim` — recommend a simulation backend (for example\n  `nvidia` for a single GPU, or `nvidia --target-option mgpu` for circuits\n  larger than one GPU's memory).\n- `\u002Fcudaq-guide qpu` — start the two-step QPU dialogue (technology, then\n  provider) and read the matching hardware doc.\n- `\u002Fcudaq-guide parallelize` — choose between `mgpu` (pool memory for one large\n  circuit) and `mqpu` (run many circuits in parallel).\n\n---\n\n## Limitations\n\n- GPU simulation requires Linux (x86_64 or ARM64); macOS is CPU-only\n- Multi-GPU `mgpu` target requires MPI\n- Kernel code must use a restricted Python subset; NumPy\u002FSciPy are not\n  allowed inside kernels\n- QPU access requires provider-specific credentials and accounts\n\n## Troubleshooting\n\n- Import error after `pip install cudaq`: Ensure Python 3.10+ and a\n  supported OS (Linux or macOS)\n- No GPU detected: Verify CUDA Toolkit is installed and `nvidia-smi`\n  shows your GPU; fall back to `qpp-cpu`\n- Kernel compile error: Check that only supported Python constructs are\n  used inside `@cudaq.kernel`\n- QPU submission fails: Confirm credentials are set as environment\n  variables per the provider docs\n",{"data":32,"body":51},{"name":4,"title":33,"description":6,"version":34,"author":35,"tags":36,"tools":40,"license":23,"compatibility":44,"metadata":45},"Cuda Quantum","1.0.1","CUDA-Q Team \u003Ccuda-quantum@nvidia.com>",[37,19,38,39,8],"cuda-quantum","onboarding","getting-started",[41,42,43],"Read","Glob","Grep","Python 3.10+, C++ 20",{"author":35,"tags":46,"languages":47,"domain":50},[37,19,38,39,8],[48,49],"python","c++","quantum",{"type":52,"children":53},"root",[54,63,78,84,89,95,133,139,168,174,332,338,479,483,489,494,506,509,513,517,570,575,674,677,681,686,778,783,807,835,838,843,856,863,1022,1025,1029,1034,1039,1048,1053,1348,1353,1394,1397,1401,1406,1518,1521,1525,1530,1629,1649,1669,1766,1772,1793,1854,1859,1862,1867,1978,1981,1987,2017,2023,2071],{"type":55,"tag":56,"props":57,"children":59},"element","h2",{"id":58},"cuda-q-getting-started-guide",[60],{"type":61,"value":62},"text","CUDA-Q Getting Started Guide",{"type":55,"tag":64,"props":65,"children":66},"p",{},[67,69,76],{"type":61,"value":68},"You are a CUDA-Q expert assistant. 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not\nallowed inside kernels",{"type":55,"tag":100,"props":2013,"children":2014},{},[2015],{"type":61,"value":2016},"QPU access requires provider-specific credentials and accounts",{"type":55,"tag":56,"props":2018,"children":2020},{"id":2019},"troubleshooting",[2021],{"type":61,"value":2022},"Troubleshooting",{"type":55,"tag":96,"props":2024,"children":2025},{},[2026,2038,2056,2066],{"type":55,"tag":100,"props":2027,"children":2028},{},[2029,2031,2036],{"type":61,"value":2030},"Import error after ",{"type":55,"tag":70,"props":2032,"children":2034},{"className":2033},[],[2035],{"type":61,"value":588},{"type":61,"value":2037},": Ensure Python 3.10+ and a\nsupported OS (Linux or macOS)",{"type":55,"tag":100,"props":2039,"children":2040},{},[2041,2043,2049,2051],{"type":61,"value":2042},"No GPU detected: Verify CUDA Toolkit is installed and ",{"type":55,"tag":70,"props":2044,"children":2046},{"className":2045},[],[2047],{"type":61,"value":2048},"nvidia-smi",{"type":61,"value":2050},"\nshows your GPU; fall back to ",{"type":55,"tag":70,"props":2052,"children":2054},{"className":2053},[],[2055],{"type":61,"value":120},{"type":55,"tag":100,"props":2057,"children":2058},{},[2059,2061],{"type":61,"value":2060},"Kernel compile error: Check that only supported Python constructs are\nused inside ",{"type":55,"tag":70,"props":2062,"children":2064},{"className":2063},[],[2065],{"type":61,"value":697},{"type":55,"tag":100,"props":2067,"children":2068},{},[2069],{"type":61,"value":2070},"QPU submission fails: Confirm credentials are set as environment\nvariables per the provider docs",{"type":55,"tag":2072,"props":2073,"children":2074},"style",{},[2075],{"type":61,"value":2076},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":2078,"total":2174},[2079,2096,2110,2124,2136,2153,2168],{"slug":2080,"name":2080,"fn":2081,"description":2082,"org":2083,"tags":2084,"stars":20,"repoUrl":21,"updatedAt":2095},"accelerated-computing-cudf","accelerate data processing with cuDF","Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV\u002FParquet I\u002FO, nullable semantics, and multi-GPU DataFrame workloads.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2085,2088,2091,2092],{"name":2086,"slug":2087,"type":13},"Data Analysis","data-analysis",{"name":2089,"slug":2090,"type":13},"Data Engineering","data-engineering",{"name":9,"slug":8,"type":13},{"name":2093,"slug":2094,"type":13},"Performance","performance","2026-07-14T05:28:43.176466",{"slug":2097,"name":2097,"fn":2098,"description":2099,"org":2100,"tags":2101,"stars":20,"repoUrl":21,"updatedAt":2109},"aiq-deploy","deploy and manage NVIDIA AI-Q infrastructure","Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2102,2105,2108],{"name":2103,"slug":2104,"type":13},"Deployment","deployment",{"name":2106,"slug":2107,"type":13},"Infrastructure","infrastructure",{"name":9,"slug":8,"type":13},"2026-07-14T05:29:06.667109",{"slug":2111,"name":2111,"fn":2112,"description":2113,"org":2114,"tags":2115,"stars":20,"repoUrl":21,"updatedAt":2123},"aiq-research","conduct deep research with AI-Q","Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2116,2119,2120],{"name":2117,"slug":2118,"type":13},"Agents","agents",{"name":9,"slug":8,"type":13},{"name":2121,"slug":2122,"type":13},"Research","research","2026-07-14T05:28:06.816956",{"slug":2125,"name":2125,"fn":2126,"description":2127,"org":2128,"tags":2129,"stars":20,"repoUrl":21,"updatedAt":2135},"amc-run-sample-calibration","run AMC sample dataset calibration","Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2130,2131,2132],{"name":2086,"slug":2087,"type":13},{"name":9,"slug":8,"type":13},{"name":2133,"slug":2134,"type":13},"Testing","testing","2026-07-17T05:29:03.913266",{"slug":2137,"name":2137,"fn":2138,"description":2139,"org":2140,"tags":2141,"stars":20,"repoUrl":21,"updatedAt":2152},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2142,2145,2148,2149],{"name":2143,"slug":2144,"type":13},"Automation","automation",{"name":2146,"slug":2147,"type":13},"Imaging","imaging",{"name":9,"slug":8,"type":13},{"name":2150,"slug":2151,"type":13},"Video","video","2026-07-17T05:28:53.905004",{"slug":2154,"name":2154,"fn":2155,"description":2156,"org":2157,"tags":2158,"stars":20,"repoUrl":21,"updatedAt":2167},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2159,2160,2163,2164],{"name":2103,"slug":2104,"type":13},{"name":2161,"slug":2162,"type":13},"Docker","docker",{"name":9,"slug":8,"type":13},{"name":2165,"slug":2166,"type":13},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":4,"name":4,"fn":5,"description":6,"org":2169,"tags":2170,"stars":20,"repoUrl":21,"updatedAt":22},{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2171,2172,2173],{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":15,"slug":16,"type":13},305,{"items":2176,"total":2326},[2177,2195,2210,2221,2233,2247,2260,2274,2285,2294,2308,2317],{"slug":2178,"name":2178,"fn":2179,"description":2180,"org":2181,"tags":2182,"stars":2192,"repoUrl":2193,"updatedAt":2194},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2183,2186,2189],{"name":2184,"slug":2185,"type":13},"Documentation","documentation",{"name":2187,"slug":2188,"type":13},"MCP","mcp",{"name":2190,"slug":2191,"type":13},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":2196,"name":2196,"fn":2197,"description":2198,"org":2199,"tags":2200,"stars":2207,"repoUrl":2208,"updatedAt":2209},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2201,2204,2205],{"name":2202,"slug":2203,"type":13},"Containers","containers",{"name":2103,"slug":2104,"type":13},{"name":2206,"slug":48,"type":13},"Python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":2211,"name":2211,"fn":2212,"description":2213,"org":2214,"tags":2215,"stars":2207,"repoUrl":2208,"updatedAt":2220},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2216,2219],{"name":2217,"slug":2218,"type":13},"CI\u002FCD","ci-cd",{"name":2103,"slug":2104,"type":13},"2026-07-14T05:25:59.97109",{"slug":2222,"name":2222,"fn":2223,"description":2224,"org":2225,"tags":2226,"stars":2207,"repoUrl":2208,"updatedAt":2232},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2227,2228,2229],{"name":2217,"slug":2218,"type":13},{"name":2103,"slug":2104,"type":13},{"name":2230,"slug":2231,"type":13},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":2234,"name":2234,"fn":2235,"description":2236,"org":2237,"tags":2238,"stars":2207,"repoUrl":2208,"updatedAt":2246},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2239,2242,2243],{"name":2240,"slug":2241,"type":13},"Debugging","debugging",{"name":2230,"slug":2231,"type":13},{"name":2244,"slug":2245,"type":13},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":2248,"name":2248,"fn":2249,"description":2250,"org":2251,"tags":2252,"stars":2207,"repoUrl":2208,"updatedAt":2259},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2253,2256],{"name":2254,"slug":2255,"type":13},"Best Practices","best-practices",{"name":2257,"slug":2258,"type":13},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":2261,"name":2261,"fn":2262,"description":2263,"org":2264,"tags":2265,"stars":2207,"repoUrl":2208,"updatedAt":2273},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2266,2269,2272],{"name":2267,"slug":2268,"type":13},"Machine Learning","machine-learning",{"name":2270,"slug":2271,"type":13},"Migration","migration",{"name":9,"slug":8,"type":13},"2026-07-17T06:07:11.777011",{"slug":2275,"name":2275,"fn":2276,"description":2277,"org":2278,"tags":2279,"stars":2207,"repoUrl":2208,"updatedAt":2284},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2280,2283],{"name":2281,"slug":2282,"type":13},"QA","qa",{"name":2133,"slug":2134,"type":13},"2026-07-14T05:25:53.673039",{"slug":2286,"name":2286,"fn":2287,"description":2288,"org":2289,"tags":2290,"stars":2207,"repoUrl":2208,"updatedAt":2293},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2291,2292],{"name":2103,"slug":2104,"type":13},{"name":2106,"slug":2107,"type":13},"2026-07-14T05:25:49.362534",{"slug":2295,"name":2295,"fn":2296,"description":2297,"org":2298,"tags":2299,"stars":2207,"repoUrl":2208,"updatedAt":2307},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2300,2303,2304],{"name":2301,"slug":2302,"type":13},"Code Review","code-review",{"name":2230,"slug":2231,"type":13},{"name":2305,"slug":2306,"type":13},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":2309,"name":2309,"fn":2310,"description":2311,"org":2312,"tags":2313,"stars":2207,"repoUrl":2208,"updatedAt":2316},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2314,2315],{"name":2281,"slug":2282,"type":13},{"name":2133,"slug":2134,"type":13},"2026-07-14T05:25:54.928983",{"slug":2318,"name":2318,"fn":2319,"description":2320,"org":2321,"tags":2322,"stars":2207,"repoUrl":2208,"updatedAt":2325},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[2323,2324],{"name":2143,"slug":2144,"type":13},{"name":2217,"slug":2218,"type":13},"2026-07-30T05:29:03.275638",496]