caffe2

Caffe2 is a deep-learning framework designed to easily express all model types, for example, CNN, RNN, and more, in a friendly python-based API, and execute them using a highly efficiently C++ and CUDA back-end.
Caffe2 supports single and multi-GPU execution, along with support for multi-node execution.

Running cafee2:

use docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.

Procedure:

1. In the Tags section, locate the container image release that you want to run.
2. In the Pull 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. A typical command to launch the container is:

   nvidia-docker run -it --rm -v local_dir:container_dir nvcr.io/nvidia/caffe2:<xx.xx>

   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 tag. For example, 17.06.

 

 

For any queries, raise a ticket in the helpdesk or please contact System Administrator, #103,SERC.