
cuda-convnet2
This is an update to cuda-convnet. Update: this project is being moved to GitHub, since Google Code is shutting down. Click here for the GitHub project.
This project has three major new features relative to cuda-convnet: 1. Improved training times on Kepler-generation Nvidia GPUs (Geforce Titan, K20, K40). 1. Multi-GPU training support implementing data parallelism, model parallelism, and the hybrid approach described in One weird trick for parallelizing convolutional neural networks. 1. Less-polished code and incomplete (but improving) documentation.
Documentation
Usage
- Compiling -- how to compile the code
- Data -- how to generate training data
- TrainingExample -- how to train an example network
- LayerParams -- how to specify a custom network
- MultiGPU -- how to train multi-GPU networks
- ShowNet -- how to look inside trained networks
Reference
- Arguments -- listing of command-line arguments
- NeuronTypes -- listing of supported neuron activation functions
- LearningRates -- listing of supported learning rate schedules
Contact
- My email
Contributions
- This code includes contributions by Anker Guo of the Tencent BestImage team:
- 50% acceleration of batch-32 convolution kernels; 10% acceleration of batch-128 convolution kernels.
Project Information
The project was created on Jun 16, 2014.
- License: Apache License 2.0
- 160 stars
- git-based source control