version: "1.0.1" name: cupynumeric-install description: Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope. license: CC-BY-4.0 OR Apache-2.0 compatibility: linux-x86_64, linux-aarch64, wsl-x86_64 metadata: author: "NVIDIA Corporation <legate@nvidia.com>" version: "2.0.0" tags:

• cupynumeric
• legate
• numpy
• installation
• conda
• gpu
• distributed-computing

upstream: https://github.com/nv-legate/cupynumeric docs: https://docs.nvidia.com/cupynumeric/latest/installation.html

cuPyNumeric Install (user)

Purpose

Use this skill to install cuPyNumeric for use from Python and to verify the install actually works (including GPU usage). Apply it whenever a user wants cuPyNumeric running via conda or pip. Do not use it to build from source (to modify or contribute) — that is out of scope.

Mandatory rules

• Never run installs. Do not run pip install, conda install, or any installer. Print the command; let the user run it.
• Always isolate. No installs into base conda, system Python, or shared global envs.
• Detect before recommending. Read-only --version checks are fine.

Prerequisites

Confirm these system requirements before recommending any install:

• GPU: Compute Capability ≥ 7.0 (Volta+). CPU-only also supported.
• CUDA: 12.2+.
• OS: Linux (x86_64 / aarch64), Windows via WSL.
• Python: 3.11 through 3.14
• conda: ≥ 24.1 (conda path only).
• Package manager: conda (upstream-recommended) or pip. If neither is present, bootstrap one first (see Instructions).

Instructions

Follow these steps in order: confirm the prerequisites, ask the scoping questions, install via the chosen path, then verify.

Ask before installing

1. Package manager? Check conda --version and pip --version. Prefer conda (upstream-recommended); fall back to pip.
2. Env target? GPU machine, CPU-only laptop, cloud, container, or remote/server.
3. CUDA version? Ask only when forcing the GPU variant on a host without a visible GPU. Check with nvidia-smi / nvcc --version.

Bootstrap — install a package manager first

If neither conda nor pip is available, install one. Provide the command and the docs link; do not run it — curl | bash requires user trust.

Recommended: Miniforge (full conda, conda-forge default)

curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash "Miniforge3-$(uname)-$(uname -m).sh"

Docs: https://github.com/conda-forge/miniforge

Alternative: Python + pip

Install Python from your OS package manager (apt/dnf/brew) or https://www.python.org/downloads/. If pip is missing on an existing Python: python -m ensurepip --upgrade.

After installing, open a new shell so the binary is on PATH.

Install — conda path

conda create -n cupynumeric -c conda-forge -c legate cupynumeric
conda activate cupynumeric

Into an existing env: conda install -c conda-forge -c legate cupynumeric.

conda auto-selects the GPU vs CPU variant from whether nvidia-smi works at install time. To override that, see below.

Force the GPU variant

Set CONDA_OVERRIDE_CUDA only when no GPU is visible at install time (e.g. building a container for a GPU host). Use the runtime host's CUDA version:

CONDA_OVERRIDE_CUDA="12.2" conda install -c conda-forge -c legate cupynumeric

Nightly (less validated)

conda install -c conda-forge -c legate-nightly cupynumeric

Install — pip path

python -m venv .venv
source .venv/bin/activate
pip install nvidia-cupynumeric

Verify

Smoke test (always run)

Run a self-contained script through the legate launcher — no repo checkout needed.

TMP=$(mktemp -d)
cat > "$TMP/smoke.py" <<'EOF'
import cupynumeric as np
a = np.arange(10)
b = np.ones((4, 4))
print("sum:", a.sum())            # expect 45
print("matmul:", (b @ b).sum())   # expect 64.0
EOF
legate "$TMP/smoke.py"
rm -rf "$TMP"

Expect sum: 45 and matmul: 64.0. If legate is missing, the env is not activated — see Troubleshooting.

GPU usage check (mandatory when a supported GPU is present)

A passing smoke test does not prove GPU usage — a CPU-variant install on a GPU box produces correct results too. Run both steps.

1. Force a GPU launch. legate --gpus N requests N GPUs; fails fast if no GPU is visible or the CPU variant is installed.

TMP=$(mktemp -d)
cat > "$TMP/check.py" <<'EOF'
import cupynumeric as np
print(np.ones((4096, 4096)).sum())
EOF
legate --gpus 1 "$TMP/check.py"
rm -rf "$TMP"

Expect 16777216.0. If you see CUDA driver, libcudart, or no GPUs available, the CPU variant is installed; reinstall with CONDA_OVERRIDE_CUDA.

2. Confirm the GPU was touched. Run a deadline-bounded matmul loop alongside nvidia-smi, all from one shell — no second-terminal race:

TMPDIR_GPU=$(mktemp -d)
SCRIPT="$TMPDIR_GPU/cupynumeric_gpu_check.py"
cat > "$SCRIPT" <<'EOF'
import cupynumeric as np, time
a = np.ones((10000, 10000))
deadline = time.time() + 20
iters = 0
while time.time() < deadline:
    b = a @ a
    _ = float(b.sum())   # force sync so the matmul actually runs
    iters += 1
print("iters:", iters)
EOF
legate --gpus 1 "$SCRIPT" &
WORKLOAD=$!
sleep 5                                     # buffer for Legate startup
for _ in $(seq 10); do                      # 10 samples at 1s — covers slow startup
  nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv,noheader
  sleep 1
done
wait "$WORKLOAD"
rm -rf "$TMPDIR_GPU"

Expect memory.used in the GiB range across most samples and non-trivial utilization.gpu in several. If both stay at baseline across every sample, the GPU variant is not installed — check conda list cupynumeric for *_gpu (not *_cpu).

Deeper recipes

See verification_examples.md for multi-GPU checks, CPU fallback, container, and troubleshooting.

Limitations

• Don't mix conda and pip in one env. Mixing overrides the first install and breaks at import. To switch, run pip uninstall nvidia-cupynumeric or conda remove cupynumeric first.
• Use the `legate` launcher for multi-GPU / multi-rank runs. Plain python runs single-process: legate --gpus 2 script.py.
• Force the GPU variant on a CPU-only host with `CONDA_OVERRIDE_CUDA`. conda otherwise auto-selects the CPU or GPU variant from nvidia-smi at install time.
• Require Volta or newer. Pascal (GTX 10xx / P100) is unsupported.
• Verify `conda --version` ≥ 24.1. Older releases silently break variant selection.
• Treat multi-node / MPI / UCX as out of scope. Defer to https://docs.nvidia.com/legate/latest/networking-wheels.html and https://docs.nvidia.com/legate/latest/mpi-wrapper.html.

Troubleshooting

• `ModuleNotFoundError: No module named 'cupynumeric'` → Run which python and pip list | grep cupynumeric (or conda list | grep cupynumeric) from the same shell to find the env mismatch.
• `ImportError` mentioning CUDA / `libcudart` → Reinstall with CONDA_OVERRIDE_CUDA="<your-cuda-version>"; the CPU variant is on a GPU box, or CUDA versions are mismatched.
• `legate: command not found` → Activate the env, then run which legate to confirm.
• Slower than NumPy on a laptop → Expect this for small problems (Legate per-task overhead). See the cuPyNumeric FAQ.

See also

• references/verification_examples.md — verification + troubleshooting recipes.
• Upstream docs: https://docs.nvidia.com/cupynumeric/latest/installation.html
• Legate requirements: https://docs.nvidia.com/legate/latest/installation.html