• Stars
    star
    333
  • Rank 126,599 (Top 3 %)
  • Language
    Jupyter Notebook
  • License
    Apache License 2.0
  • Created over 3 years ago
  • Updated 3 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

cuCIM - RAPIDS GPU-accelerated image processing library

ย cuCIM

RAPIDS cuCIM is an open-source, accelerated computer vision and image processing software library for multidimensional images used in biomedical, geospatial, material and life science, and remote sensing use cases.

cuCIM offers:

  • Enhanced Image Processing Capabilities for large and n-dimensional tag image file format (TIFF) files
  • Accelerated performance through Graphics Processing Unit (GPU)-based image processing and computer vision primitives
  • A Straightforward Pythonic Interface with Matching Application Programming Interface (API) for Openslide

cuCIM supports the following formats:

  • Aperio ScanScope Virtual Slide (SVS)
  • Philips TIFF
  • Generic Tiled, Multi-resolution RGB TIFF files with the following compression schemes:
    • No Compression
    • JPEG
    • JPEG2000
    • Lempel-Ziv-Welch (LZW)
    • Deflate

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

Developer Page

Blogs

Webinars

Documentation

Release notes are available on our wiki page.

Install cuCIM

Conda

Conda (stable)

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

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

Conda (nightlies)

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

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

PyPI

pip install cucim

# Install dependencies for `cucim.skimage` (assuming that CUDA 11.0 is used for CuPy)
pip install scipy scikit-image cupy-cuda110

Notebooks

Please check out our Welcome notebook (NBViewer)

Downloading sample images

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}

Build/Install from Source

See build instructions.

Contributing Guide

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.

Acknowledgments

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.

License

Apache-2.0 License (see LICENSE file).

Copyright (c) 2020-2022, NVIDIA CORPORATION.

More Repositories

1

cudf

cuDF - GPU DataFrame Library
C++
8,319
star
2

cuml

cuML - RAPIDS Machine Learning Library
C++
3,864
star
3

cugraph

cuGraph - RAPIDS Graph Analytics Library
Cuda
1,668
star
4

cusignal

cuSignal - RAPIDS Signal Processing Library
Python
703
star
5

raft

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.
Cuda
586
star
6

jupyterlab-nvdashboard

A JupyterLab extension for displaying dashboards of GPU usage.
TypeScript
582
star
7

notebooks

RAPIDS Sample Notebooks
Shell
577
star
8

cuspatial

CUDA-accelerated GIS and spatiotemporal algorithms
Jupyter Notebook
543
star
9

rmm

RAPIDS Memory Manager
C++
420
star
10

deeplearning

Jupyter Notebook
336
star
11

dask-cuda

Utilities for Dask and CUDA interactions
Python
266
star
12

cuxfilter

GPU accelerated cross filtering with cuDF.
Python
261
star
13

node

GPU-accelerated data science and visualization in node
TypeScript
170
star
14

clx

A collection of RAPIDS examples for security analysts, data scientists, and engineers to quickly get started applying RAPIDS and GPU acceleration to real-world cybersecurity use cases.
Jupyter Notebook
167
star
15

libgdf

[ARCHIVED] C GPU DataFrame Library
Cuda
138
star
16

dask-cudf

[ARCHIVED] Dask support for distributed GDF object --> Moved to cudf
Python
135
star
17

cloud-ml-examples

A collection of Machine Learning examples to get started with deploying RAPIDS in the Cloud
Jupyter Notebook
134
star
18

ucx-py

Python bindings for UCX
Python
118
star
19

gpu-bdb

RAPIDS GPU-BDB
Python
103
star
20

kvikio

KvikIO - High Performance File IO
Python
100
star
21

plotly-dash-rapids-census-demo

Jupyter Notebook
92
star
22

gputreeshap

C++
83
star
23

frigate

Frigate is a tool for automatically generating documentation for your Helm charts
Python
76
star
24

wholegraph

WholeGraph - large scale Graph Neural Networks
Cuda
75
star
25

spark-examples

[ARCHIVED] Moved to github.com/NVIDIA/spark-xgboost-examples
Jupyter Notebook
70
star
26

docker

Dockerfile templates for creating RAPIDS Docker Images
Shell
69
star
27

cuvs

cuVS - a library for vector search and clustering on the GPU
Jupyter Notebook
57
star
28

custrings

[ARCHIVED] GPU String Manipulation --> Moved to cudf
Cuda
46
star
29

docs

RAPIDS Documentation Site
HTML
34
star
30

cudf-alpha

[ARCHIVED] cuDF [alpha] - RAPIDS Merge of GoAi into cuDF
34
star
31

rapids-examples

Jupyter Notebook
31
star
32

nvgraph

C++
26
star
33

rapids-cmake

CMake
24
star
34

cuhornet

Cuda
24
star
35

cuDataShader

Jupyter Notebook
22
star
36

gpuci-build-environment

Common build environment used by gpuCI for building RAPIDS
Dockerfile
19
star
37

distributed-join

C++
19
star
38

devcontainers

Shell
18
star
39

dask-cuml

[ARCHIVED] Dask support for multi-GPU machine learning algorithms --> Moved to cuml
Python
16
star
40

integration

RAPIDS - combined conda package & integration tests for all of RAPIDS libraries
Shell
15
star
41

xgboost-conda

Conda recipes for xgboost
Jupyter Notebook
12
star
42

benchmark

Python
11
star
43

ucxx

C++
11
star
44

dependency-file-generator

Python
10
star
45

asvdb

Python
9
star
46

helm-chart

Shell
9
star
47

deployment

RAPIDS Deployment Documentation
Jupyter Notebook
9
star
48

miniforge-cuda

Dockerfile
9
star
49

ci-imgs

Dockerfile
7
star
50

dask-cugraph

Python
7
star
51

rapids.ai

rapids.ai web site
HTML
7
star
52

ptxcompiler

Python
6
star
53

GaaS

Python
5
star
54

rvc

Go
4
star
55

scikit-learn-nv

Python
4
star
56

ops-bot

A Probot application used by the Ops team for automation.
TypeScript
4
star
57

workflows

Shell
4
star
58

rapids-triton

C++
4
star
59

dask-build-environment

Build environments for various dask related projects on gpuCI
Dockerfile
3
star
60

roc

GitHub utilities for the RAPIDS Ops team
Go
3
star
61

multi-gpu-tools

Shell
3
star
62

detect-weak-linking

Python
3
star
63

dask-cuda-benchmarks

Python
2
star
64

shared-workflows

Reusable GitHub Actions workflows for RAPIDS CI
Shell
2
star
65

rapids_triton_pca_example

C++
2
star
66

cugunrock

Cuda
2
star
67

dgl-cugraph-build-environment

Dockerfile
2
star
68

projects

Jupyter Notebook
2
star
69

crossfit

Metric calculation library
Python
2
star
70

gpuci-mgmt

Mangement scripts for gpuCI
Shell
1
star
71

ansible-roles

1
star
72

code-share

C++
1
star
73

build-metrics-reporter

Python
1
star
74

cibuildwheel-imgs

Dockerfile
1
star
75

gpuci-tools

User tools for use within the gpuCI environment
Shell
1
star
76

pynvjitlink

Python
1
star
77

rapids-dask-dependency

Shell
1
star
78

sphinx-theme

This repository contains a Sphinx theme used for RAPIDS documentation
CSS
1
star