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5fd21dd7fa
Add a shared nvbench_tooling_deps helper for importing packages required by NVBench console tools. Missing tooling packages now raise a dedicated error with an install recipe instead of failing with a raw ImportError. Update script imports to work both as installed package modules and as direct source-tree scripts by using the __package__ import pattern for nvbench_json and the new tooling helper. Defer nvbench-compare dependencies to the points where they are needed: NumPy/colorama during normal comparison setup, tabulate during table rendering, jsondiff only for device mismatch reporting, and plotting packages only for plot modes. Update tests to initialize compare tooling when calling internals directly and add coverage for the tooling dependency loader. Closes #384
CUDA Kernel Benchmarking Package
This package provides a Python API to the CUDA Kernel Benchmarking
Library NVBench.
Installation
Install from PyPi
pip install cuda-bench[cu13] # For CUDA 13.x
pip install cuda-bench[cu12] # For CUDA 12.x
Building from source
Ensure recent version of CMake
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