CV-CUDA Samples

Welcome to the CV-CUDA samples documentation! These samples demonstrate how to use CV-CUDA for GPU-accelerated computer vision and deep learning workflows.

Overview

CV-CUDA samples showcase the usage of CV-CUDA operators for GPU-accelerated computer vision workflows via simple single-operator examples or complete end-to-end deep learning pipelines.

Sample Categories

Operators

Focused examples of individual CV-CUDA operations. Great for learning specific functionality and experimenting with parameters.

Applications

Complete end-to-end pipelines combining preprocessing, inference, and post-processing.

Walkthrough Guide

Installation

The easiest way to get started is to use the installation script that automatically detects your CUDA version and installs the appropriate dependencies (including CV-CUDA).

Option 1: Using the Installation Script (Recommended)

cd samples
./install_samples_dependencies.sh

This script will:

  • Detect your CUDA version (12 or 13)

  • Create a virtual environment at venv_samples

  • Install all required dependencies including CV-CUDA, PyTorch, NumPy, and sample-specific packages

After installation, activate the virtual environment:

source venv_samples/bin/activate

For interoperability samples, see Setting Up the Environment.

Option 2: Build from Source

Alternatively, you can build CV-CUDA from source and install the remaining dependencies. Follow the installation guide, then use the installation script which will automatically use your local build:

cd samples
./install_samples_dependencies.sh
# Optionally install your local wheel over the PyPI version
source venv_samples/bin/activate
python3 -m pip install --force-reinstall ../build-rel/python3/repaired_wheels/cvcuda-*.whl

CV-CUDA Hello World

Once you have installed the dependencies, run the Hello World sample:

python3 samples/applications/hello_world.py

This simple example demonstrates the fundamental CV-CUDA workflow:

  • Reading from disk straight to GPU (no CPU-GPU copies)

  • Resizing and batching images for parallel processing

  • Applying operations (Gaussian blur) on the entire batch

  • Writing to disk from GPU (no CPU-GPU copies)

What You’ll See:

The sample loads an image, resizes it to 224×224, applies a Gaussian blur, and saves the result to .cache/cat_hw.jpg.

Try It with Your Own Image:

python3 samples/applications/hello_world.py -i your_image.jpg -o output.jpg

Want to Learn More?

See the complete Hello World documentation for detailed explanations of each step.

Running Operator and Application Samples

To test all samples at once:

./samples/run_samples.sh

This script runs every sample with default parameters.

Next Steps

Now that you’ve explored the basics:

  1. Try More Samples: Experiment with different operators and applications

  2. Modify for Your Use Case: Adapt samples for your specific needs

  3. Read the API Documentation: Explore the full Python API

  4. Build Your Pipelines: Use sample patterns in your applications

Sample Index

Quick access to all CV-CUDA sample documentation.

Applications

Operators

Common Utilities

Additional Resources

See Also