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- Added Python samples for CUDA Python 1.0 release - Renamed top-level `Samples` directory to `cpp` to accommodate Python samples.
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3.2 KiB
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.coreAPI 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 APInumpy- Array operations and DLPack supportcupy- GPU array operations and CUDA interoperability
Key APIs
From cuda.core:
Device()- Initialize and access CUDA deviceDevice.set_current()- Set the current device for API callsDevice.create_stream()- Create CUDA stream for async operationsDevice.memory_resource- Access device memory allocatorPinnedMemoryResource()- Allocate pinned host memorybuffer.copy_to()- Copy data between memory spacesbuffer.close()- Release allocated memory
From numpy:
np.from_dlpack()- Create array view from DLPack capsulenp.copyto()- Copy data between arrays
From cupy:
cp.from_dlpack()- Create GPU array view from DLPack capsulecp.cuda.ExternalStream()- 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-pythonpackage (>=13.0.0+)cuda-corepackage (>=0.6.0)cupy-cuda13xpackage (13.0.0+)
Installation
Install the required packages from requirements.txt:
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(>=0.6.0)cupy-cuda13x(13.0.0+)
How to Run
Basic usage:
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 APIREADME.md- This filerequirements.txt- Sample dependencies../../Utilities/cuda_samples_utils.py- Common utilities (imported by this sample)