Installation

Per-library install commands, CUDA version selection, and environment setup.

Use uv add in standalone examples to match this repository's convention. If the user's project already uses another package manager, follow that project rather than rewriting its tooling.

RAPIDS packages below track RAPIDS 26.06 (June 2026): they require Python >= 3.11 and CUDA 12.x or 13.x. Every maintained RAPIDS package ships -cu12 and -cu13 wheel variants; the examples use -cu12, so substitute -cu13 for CUDA 13 systems. Use the RAPIDS release selector to confirm driver, Python, CUDA, and package compatibility. The NVIDIA extra index is included consistently because package availability differs; many packages are also mirrored directly on PyPI.

bash

# CuPy (choose the right CUDA version; CuPy 14+ supports CUDA 12/13 only)
uv add "cupy-cuda12x==14.1.*"          # For CUDA 12.x
uv add "cupy-cuda13x==14.1.*"          # For CUDA 13.x

# Numba-CUDA compatibility path (maintenance mode; installs numba automatically)
uv add "numba-cuda[cu12]==0.30.*"      # use [cu13] for CUDA 13
# For new kernel projects, evaluate Numba-CUDA-MLIR and its migration guide first.

# Warp (simulation, spatial computing, differentiable programming)
uv add "warp-lang==1.15.*"              # CUDA 12 runtime included; CUDA 13 builds are on GitHub Releases only

# cuDF (RAPIDS)
uv add --extra-index-url=https://pypi.nvidia.com "cudf-cu12==26.6.*"
# For cudf.pandas accelerator mode, that's all you need
# Load it with: python -m cudf.pandas your_script.py

# cuML (RAPIDS machine learning)
uv add --extra-index-url=https://pypi.nvidia.com "cuml-cu12==26.6.*"
# For cuml.accel accelerator mode (zero-change sklearn acceleration):
# Load it with: python -m cuml.accel your_script.py

# cuGraph (RAPIDS graph analytics) — NVIDIA index still required (PyPI has only stub packages)
uv add --extra-index-url=https://pypi.nvidia.com "cugraph-cu12==26.6.*"    # Core cuGraph
uv add --extra-index-url=https://pypi.nvidia.com "nx-cugraph-cu12==26.6.*" # NetworkX backend
# For nx-cugraph zero-change NetworkX acceleration:
# NX_CUGRAPH_AUTOCONFIG=True python your_script.py

# KvikIO (high-performance GPU file IO)
uv add --extra-index-url=https://pypi.nvidia.com "kvikio-cu12==26.6.*"
# Optional: uv add "zarr==3.*"   # For Zarr GPU backend support

# cuxfilter (interactive dashboards) — SUNSET: 26.06 is the final release
uv add --extra-index-url=https://pypi.nvidia.com "cuxfilter-cu12==26.6.*"
# Depends on cuDF — installs it automatically

# cuCIM (RAPIDS image processing — scikit-image on GPU)
uv add --extra-index-url=https://pypi.nvidia.com "cucim-cu12==26.6.*"

# cuVS (RAPIDS vector search)
uv add --extra-index-url=https://pypi.nvidia.com "cuvs-cu12==26.6.*"

# cuSpatial (geospatial) — ARCHIVED: frozen at 25.04, pins cudf-cu12==25.4.*
# Install only in a dedicated environment; NVIDIA index required
uv add --extra-index-url=https://pypi.nvidia.com "cuspatial-cu12==25.4.*"

# RAFT (low-level GPU primitives)
uv add --extra-index-url=https://pypi.nvidia.com "pylibraft-cu12==26.6.*"   # Core primitives
uv add --extra-index-url=https://pypi.nvidia.com "raft-dask-cu12==26.6.*"   # Multi-GPU support (optional)

To check CUDA availability after installation:

python

# CuPy
import cupy as cp
print(cp.cuda.runtime.getDeviceCount())  # Should be >= 1

# Numba
from numba import cuda
print(cuda.is_available())               # Should be True
print(cuda.detect())                     # Shows GPU details

# cuDF
import cudf
print(cudf.Series([1, 2, 3]))           # Should print a GPU series

# cuML
import cuml
print(cuml.__version__)                  # Should print version

# cuGraph
import cugraph
print(cugraph.__version__)               # Should print version

# Warp
import warp as wp
wp.init()                                # Should print device info

# KvikIO
import kvikio
import kvikio.cufile_driver
print(kvikio.cufile_driver.get("is_gds_available"))  # True if GDS is set up

# cuxfilter
import cuxfilter
print(cuxfilter.__version__)             # Should print version

# cuCIM
from cucim.skimage.filters import gaussian
import cupy as cp
print(gaussian(cp.zeros((8, 8), dtype=cp.float32), sigma=1).shape)

# cuVS
from cuvs.neighbors import cagra
import cupy as cp
dataset = cp.random.rand(1000, 128, dtype=cp.float32)
index = cagra.build(cagra.IndexParams(), dataset)
print("cuVS working")                    # Should print confirmation

# cuSpatial
import cuspatial
from shapely.geometry import Point
gs = cuspatial.GeoSeries([Point(0, 0)])
print("cuSpatial working")              # Should print confirmation

# RAFT (pylibraft)
from pylibraft.common import DeviceResources
handle = DeviceResources()
handle.sync()
print("pylibraft is working")