What is Torch?

Torch is a scientific computing framework with wide support for deep learning algorithms. Torch is easy to use and efficient, thanks to an easy and fast scripting language, Lua, and an underlying C/CUDA implementation.

Torch offers popular neural network and optimization libraries that are easy to use yet provide maximum flexibility to build complex neural network topologies.

Running Torch

Before you can run an NGC deep learning framework container, your Docker environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in the Running A Container chapter in the NVIDIA Containers And Frameworks User Guide and specify the registry, repository, and tags. For more information about using NGC, refer to the NGC Container User Guide.

The method implemented in your system depends on the DGX OS version installed (for DGX systems), the specific NGC Cloud Image provided by a Cloud Service Provider, or the software that you have installed in preparation for running NGC containers on TITAN PCs, Quadro PCs, or vGPUs.

Procedure

1. Select the Tags tab and locate the container image release that you want to run.

2. In the Pull Tag column, click the icon to copy the docker pull command.

3. Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.

4. Run the container image. To run the container, choose interactive mode or non-interactive mode.

   a. Interactive mode: Open a command prompt and issue:

   docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/torch:xx.xx
   

   b. Non-interactive mode: Open a command prompt and issue:

   docker run --gpus all --rm -v local_dir:container_dir nvcr.io/nvidia/torch:xx.xx command
   

   Where:

   • -it means run in interactive mode

   • --rm will delete the container when finished

   • -v is the mounting directory

   • local_dir is the directory or file from your host system (absolute path) that you want to access from inside your container. For example, the local_dir in the following path is /home/jsmith/data/mnist.

     -v /home/jsmith/data/mnist:/data/mnist
     

     If you are inside the container, for example, ls /data/mnist, you will see the same files as if you issued the ls /home/jsmith/data/mnist command from outside the container.

   • container_dir is the target directory when you are inside your container. For example, /data/mnist is the target directory in the example:

     -v /home/jsmith/data/mnist:/data/mnist
     

   • xx.xx is the container version. For example, 18.01.

   • command is the command you want to run in the image.

   You might want to pull in data and model descriptions from locations outside the container for use by Torch. To accomplish this, the easiest method is to mount one or more host directories as Docker data volumes. You have pulled the latest files and run the container image.

   Note: DIGITS uses shared memory to share data between processes. For example, if you use Torch multiprocessing for multi-threaded data loaders, the default shared memory segment size that the container runs with may not be enough. Therefore, you should increase the shared memory size by issuing either:

   --ipc=host
   

   or

   --shm-size=
   

   in the command line to:

   docker run --gpus all
   

5. See /workspace/README.md inside the container for information on customizing your Torch image.

Suggested Reading

For the latest Release Notes, see the Torch Release Notes Documentation website.

For more information about Torch, see:

• Torch website
• Torch project