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SPEED UP DEEP LEARNING TRAINING WITH GPU-ACCELERATED TORCH
The fastest, easiest way to get started with Torch on GPUs.
GPU-ACCELERATED TORCH

Once Torch has been installed on your system, it can be run as follows:

5 > th
 ______             __   |  Torch7                                         
/_  __/__  ________/ /   |  Scientific computing for Lua.
 / / / _ \/ __/ __/ _ \  |  Type ? for help                                
/_/  \___/_/  \__/_//_/  |  https://github.com/torch         
                         |  http://torch.ch                  
   
th>

We'll use ResNet as an example here. For instructions on ResNet module and training images data set downloading and installation, please visit Facebook ResNet Training page.

So, let's assume you have successfully installed both.

To run the training, go to the directory of ResNet clone and run:

main.lua

By default, the script runs ResNet-34 on ImageNet with a single GPU and two data-loader threads:

th main.lua -data [imagenet-folder with train and val folders]

To train ResNet-50 on four GPUs and eight CPU threads:

th main.lua -depth 50 -batchSize 256 -nGPU 4 -nThreads 8 -shareGradInput true -data [imagenet-folder]

Trained models and additional resources are available from the ResNet Training Page.

SEE HOW THE PERFORMANCE STACKS UP