cunn
CUDA backend implementation for Torch's neural network package, enabling GPU acceleration for deep learning models.
What is cunn?
cunn is a CUDA backend for Torch's neural network package that provides GPU-accelerated implementations of neural network modules. It enables deep learning researchers and developers to run their models on NVIDIA GPUs for significantly faster training and inference. The package maintains API compatibility with the CPU-based nn package, allowing easy migration of existing models to GPU.
Deep learning researchers and developers using the Torch framework who need GPU acceleration for training and deploying neural network models, particularly those working with large datasets or complex architectures.
Developers choose cunn because it provides seamless GPU acceleration for Torch models with minimal code changes, maintaining full compatibility with the existing nn API while delivering significant performance improvements through CUDA implementation.
Overview
cunn is a CUDA backend for Torch's neural network package (nn), providing GPU-accelerated implementations of neural network modules. It allows researchers and developers to significantly speed up training and inference of deep learning models by leveraging NVIDIA GPUs.
Key Features
- GPU Module Implementations — CUDA versions of most modules from the base nn package for accelerated computation
- Additional GPU Modules — Extra GPU-specific neural network components not available in the CPU-only package
- Seamless Integration — Simple conversion of existing models to GPU via
:cuda()method calls - Native CudaTensor Support — Direct creation and manipulation of GPU tensors for efficient memory management
- Performance Optimization — Guidelines for batch processing and memory reuse to maximize GPU utilization
Philosophy
cunn follows the philosophy of providing transparent GPU acceleration while maintaining compatibility with Torch's existing neural network API, allowing users to leverage GPU power with minimal code changes.
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