RAPIDS cuCIM is an extensible toolkit designed to provide GPU accelerated I/O, computer vision & image processing primitives for N-Dimensional images with a focus on biomedical imaging.

NOTE: For the latest stable README.md ensure you are on the main branch.

• GTC 2021 cuCIM: A GPU Image I/O and Processing Toolkit [S32194]
  • video

conda create -n cucim -c rapidsai -c conda-forge/label/cupy_rc -c conda-forge cucim cudatoolkit=<CUDA version>

<CUDA version> should be 11.0+ (e.g., 11.0, 11.2, etc.)

NOTE: The first cuCIM conda package (v0.19.0) would be available on 4/19/2021.

conda create -n cucim -c rapidsai-nightly -c conda-forge/label/cupy_rc -c conda-forge cucim cudatoolkit=<CUDA version>

<CUDA version> should be 11.0+ (e.g., 11.0, 11.2, etc)

Please check out our Welcome notebook.

To download images used in the notebooks, please execute the following commands from the repository root folder to copy sample input images into notebooks/input folder:

(You will need Docker installed in your system)

./run download_testdata

or

mkdir -p notebooks/input
tmp_id=$(docker create gigony/svs-testdata:little-big)
docker cp $tmp_id:/input notebooks
docker rm -v ${tmp_id}

See build instructions.

Contributions to cuCIM are more than welcome! Please review the CONTRIBUTING.md file for information on how to contribute code and issues to the project.

Without awesome third-party open source software, this project wouldn't exist.

Please find LICENSE-3rdparty.md to see which third-party open source software is used in this project.

Apache-2.0 License (see LICENSE file).

Copyright (c) 2020-2021, NVIDIA CORPORATION.