Since nvbench requires a rather new version of CMake (>=3.30.4), either build CMake from sources, or create a conda environment with a recent version of CMake, using

conda create -n build_env --yes  cmake ninja
conda activate build_env

Ensure CUDA compiler

Since building NVBench library requires CUDA compiler, ensure that appropriate environment variables are set. For example, assuming CUDA toolkit is installed system-wide, and assuming Ampere GPU architecture:

export CUDACXX=/usr/local/cuda/bin/nvcc
export CUDAARCHS=86

Build Python project

Now switch to python folder, configure and install NVBench library, and install the package in editable mode:

cd nvbench/python
pip install -e .

Verify that package works

python test/run_1.py

Run examples

# Example benchmarking numba.cuda kernel
python examples/throughput.py

# Example benchmarking kernels authored using cuda.core
python examples/axes.py

# Example benchmarking algorithms from cuda.cccl.parallel
python examples/cccl_parallel_segmented_reduce.py

# Example benchmarking CuPy function
python examples/cupy_extract.py