caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind.

NVIDIA Caffe, also known as NVCaffe, is an NVIDIA-maintained fork of BVLC Caffe tuned for NVIDIA GPUs, particularly in multi-GPU configurations.

Running cafee: Before running the container, 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. To run the container, choose interactive mode or non-interactive mode.

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

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

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

  nvidia-docker run --rm -v local_dir:container_dir nvcr.io/nvidia/caffe:<xx.xx> caffe train …

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.
  • container_dir is the target directory when you are inside your container.
  • <xx.xx> is the tag. For example, 17.06.

Note: In order to share data between ranks, NCCL may require shared system memory for IPC and pinned (page-locked) system memory resources. The operating system’s limits on these resources may need to be increased accordingly. Refer to your system’s documentation for details. In particular, Docker containers default to limited shared and pinned memory resources. When using NCCL inside a container, it is recommended that you increase these resources by issuing:

--shm-size=1g --ulimit memlock=-1 , in the command line to nvidia-docker run.

 

 

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