cuopt-install
Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.
git clone --depth 1 https://github.com/NVIDIA/skills /tmp/cuopt-install && cp -r /tmp/cuopt-install/skills/cuopt-install ~/.claude/skills/cuopt-installSKILL.md
# cuOpt Install (user) Install cuOpt to *use* it from Python, C, or as a REST server. For building cuOpt from source to contribute or modify it, see `cuopt-developer`. ## System requirements - **GPU**: NVIDIA Compute Capability ≥ 7.0 (Volta or newer). Examples: V100, A100, H100, RTX 20xx/30xx/40xx. Not supported: GTX 10xx (Pascal). - **CUDA**: 12.x or 13.x. The package CUDA suffix must match the runtime CUDA (e.g. `cuopt-cu12` / `libcuopt-cu12` with CUDA 12). - **Driver**: NVIDIA driver compatible with the CUDA version. - `cuopt-cuXX` (Python) depends on `libcuopt-cuXX` (C), so installing the Python package also installs the C library and headers. Installing `libcuopt-cuXX` on its own does **not** install the Python API. ## Required questions Ask these if not already clear: 1. **Interface** — Python, C, or REST server? Server can be called from any language via HTTP. 2. **CUDA version** — What is installed? Check with `nvcc --version` or `nvidia-smi`. 3. **Package manager** — pip, conda, or Docker preferred? 4. **Environment** — Local machine with GPU, cloud instance, Docker/Kubernetes, or remote/server (no local GPU)? ## Python API **Choose one** — do not run both. The second install would override the first and can cause CUDA / package mismatch. ### pip - **CUDA 13.x:** ```bash pip install --extra-index-url=https://pypi.nvidia.com cuopt-cu13 ``` - **CUDA 12.x:** ```bash pip install --extra-index-url=https://pypi.nvidia.com 'cuopt-cu12==26.2.*' ``` ### conda ```bash conda install -c rapidsai -c conda-forge -c nvidia cuopt ``` ### Verify ```python import cuopt print(cuopt.__version__) from cuopt import routing dm = routing.DataModel(n_locations=3, n_fleet=1, n_orders=2) ``` ## C API The C API ships in `libcuopt-cuXX`, which is also pulled in as a dependency of `cuopt-cuXX` — so if you already installed the Python package, the C library and headers are already present. Install `libcuopt` standalone only when you want the C API without Python. **Choose one** of pip or conda — do not run both. ### pip - **CUDA 13.x:** ```bash pip install --extra-index-url=https://pypi.nvidia.com libcuopt-cu13 ``` - **CUDA 12.x:** ```bash pip install --extra-index-url=https://pypi.nvidia.com 'libcuopt-cu12==26.2.*' ``` ### conda ```bash conda install -c rapidsai -c conda-forge -c nvidia libcuopt ``` ### Verify See [`references/verification_examples.md`](references/verification_examples.md) for the canonical C-API header/library `find` commands (conda and pip/venv variants). ## Server (REST) ### pip ```bash pip install --extra-index-url=https://pypi.nvidia.com cuopt-server-cu12 cuopt-sh-client ``` ### conda ```bash conda install -c rapidsai -c conda-forge -c nvidia cuopt-server cuopt-sh-client ``` ### Docker ```bash docker pull nvidia/cuopt:latest-cuda12.9-py3.13 docker run --gpus all -it --rm -p 8000:8000 nvidia/cuopt:latest-cuda12.9-py3.13 ``` ### Verify ```bash python -m cuopt_server.cuopt_service --ip 0.0.0.0 --port 8000 & sleep 5 curl -s http://localhost:8000/cuopt/health | jq . ``` ## Common Issues - `No module named 'cuopt'` → check `pip list | grep cuopt`, `which python`, reinstall with the correct extra-index-url. - CUDA not available → run `nvidia-smi` and `nvcc --version`; ensure the package CUDA suffix (`cu12` vs `cu13`) matches the installed CUDA. - Python vs C → `cuopt-cuXX` pulls in `libcuopt-cuXX` as a transitive dependency, so the C library (`libcuopt.so`) and headers (`cuopt_c.h`) are already available after installing the Python package. The reverse is **not** true: `libcuopt-cuXX` alone does not install the Python bindings. ## See also - [verification_examples.md](references/verification_examples.md) — full verification recipes for Python, C, server, and Docker. - `cuopt-developer` — build cuOpt from source and contribute to the codebase.
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Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs 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'.
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/live streams, use amc-run-rtsp-calibration instead.
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.