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Sample: Image Array Copy to GPU (Python)

python/1_GettingStarted/copyImageArraytoGPU/README.md

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Sample: Image Array Copy to GPU (Python)

Description

Copy image arrays between CPU and GPU memory using the modern cuda.core API with optimal performance through pinned memory and asynchronous transfers.

What You'll Learn

  • How to use pinned memory for faster CPU↔GPU transfers
  • Using the cuda.core API for memory management
  • Working with DLPack for zero-copy array views
  • Performing asynchronous memory transfers with CUDA streams
  • Interoperability between CUDA Core API and CuPy
  • Proper CUDA resource management and cleanup

Key Libraries

  • cuda.core - Modern CUDA Python API
  • numpy - Array operations and DLPack support
  • cupy - GPU array operations and CUDA interoperability

Key APIs

From cuda.core:

  • Device() - Initialize and access CUDA device
  • Device.set_current() - Set the current device for API calls
  • Device.create_stream() - Create CUDA stream for async operations
  • Device.memory_resource - Access device memory allocator
  • PinnedMemoryResource() - Allocate pinned host memory
  • buffer.copy_to() - Copy data between memory spaces
  • buffer.close() - Release allocated memory

From numpy:

  • np.from_dlpack() - Create array view from DLPack capsule
  • np.copyto() - Copy data between arrays

From cupy:

  • cp.from_dlpack() - Create GPU array view from DLPack capsule
  • cp.cuda.Stream.from_external() - Use external CUDA stream

From cuda_samples_utils:

  • verify_array_result() - Verify computation results

Requirements

Hardware:

  • NVIDIA GPU with CUDA support
  • Sufficient GPU memory for image data (sample uses ~200KB for 256×256×3 image)

Software:

  • CUDA Toolkit 13.0 or newer
  • Python 3.10 or newer
  • NumPy 2.3.2 or newer (required for DLPack support)
  • cuda-python package (>=13.0.0+)
  • cuda-core package (>=1.0.0)
  • cupy-cuda13x package (14.0.0+)

Installation

Install the required packages from requirements.txt:

bash
cd /path/to/cuda-samples/python/1_GettingStarted/copyImageArraytoGPU
pip install -r requirements.txt

The requirements.txt installs:

  • numpy (2.3.2+, required for DLPack)
  • cuda-python (>=13.0.0+)
  • cuda-core (>=1.0.0)
  • cupy-cuda13x (14.0.0+)

How to Run

Basic usage:

bash
cd samples/python/1_GettingStarted/copyImageArraytoGPU
python copyImageArraytoGPU.py

Expected Output

[Image Array Copy to GPU using CUDA Core API]
Device: NVIDIA GeForce RTX 4090
[Image array copy of 256x256x3 image]
Creating sample image...
Copying image to GPU...
Creating CuPy view of GPU data...
Mean pixel value (computed on GPU): 127.50
Copying image back from GPU...
Verifying result...
Test PASSED

Done

Note: Device name will vary based on your GPU.

Files

  • copyImageArraytoGPU.py - Python implementation using cuda.core API
  • README.md - This file
  • requirements.txt - Sample dependencies
  • ../../Utilities/cuda_samples_utils.py - Common utilities (imported by this sample)

See Also