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:
- In the Tags section, locate the container image release that you want to run.
- In the Pull column, click the icon to copy the
docker pull
command. - 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.
- 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 directorylocal_dir
is the directory or file from your host system (absolute path) that you want to access from inside your container. For example, thelocal_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 thels /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. |