Check out the website at https://www.hydraimageprocessor.com
Hydra Image Processor is a hardware accelerated signal processing library written with CUDA. Hydra aims to create a signal processing library that can be incorporated into many software tools. This library is licensed under BSD 3-Clause to encourage use in open-source and commercial software. My only plea is that if you find bugs or make changes that you contribute them back to this repository. Happy processing and enjoy!
Hydra is distributed through conda-forge and requires an NVIDIA CUDA-capable GPU with an up-to-date driver. Install it with conda, mamba, or pixi:
# conda / mamba
conda install -c conda-forge hydra-image-processor
mamba install -c conda-forge hydra-image-processor
# pixi (per-project)
pixi add hydra-image-processorThe CUDA runtime is statically linked, so no separate CUDA toolkit is required to import the package — only an NVIDIA GPU driver to run GPU operations.
pip / uv are not supported. Hydra is a CUDA-only compiled extension with no PyPI wheels. Use a conda-based installer (conda, mamba, or pixi).
import hydra_image_processor as HIP
import numpy as np
image = np.random.rand(100, 100, 50).astype(np.float32)
smoothed = HIP.gaussian(image, sigmas=[2.0, 2.0, 1.0])
print("GPUs:", HIP.device_count())Code written against older releases that used import HIP (or import Hydra) keeps working
via compatibility shims, though import hydra_image_processor as HIP is preferred.
Source builds use scikit-build-core: a single
pip install drives CMake, compiles the CUDA extension, and installs the package. From a
conda environment that has the build tools (cmake, ninja, scikit-build-core, numpy)
plus a CUDA toolkit and C++ compiler:
pip install .See CONTRIBUTING.md for the full developer and release workflow.
https://www.hydraimageprocessor.com/quick-start
If you would like to provide feedback about this tutorial or Hydra in general, please use the forum here.