Running NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet

Before you begin

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.

About this task

On a system with GPU support for NGC containers, the following occurs when running a container:
  • The Docker engine loads the image into a container which runs the software.
  • You define the runtime resources of the container by including additional flags and settings that are used with the command. These flags and settings are described in Running A Container.
  • The GPUs are explicitly defined for the Docker container (defaults to all GPUs, but can be specified using NVIDIA_VISIBLE_DEVICES environment variable). Starting in Docker 19.03, follow the steps as outlined below. For more information, refer to the nvidia-docker documentation here.

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.


  1. Issue the command for the applicable release of the container that you want. The following command assumes you want to pull the latest container.
    docker pull
  2. 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.
  3. Run the container image. A typical command to launch the container is:
    If you have Docker 19.03 or later, a typical command to launch the container is:
    docker run --gpus all -it --rm -v local_dir:container_dir<xx.xx>-py3
    If you have Docker 19.02 or earlier, a typical command to launch the container is:
    nvidia-docker run -it --rm -v local_dir:container_dir<xx.xx>-py3
    The NVIDIA Optimized Deep Learning Framework, powered by Apache MXNet is run simply by importing it as a Python module:
    $ python
    	Python 3.5.2 (default, Nov 23 2017, 16:37:01) 
    	[GCC 5.4.0 20160609] on linux
    	Type "help", "copyright", "credits" or "license" for more information.
    	>>> import mxnet as mx
    	>>> a = mx.nd.ones((2,3), mx.gpu())
    	>>> print((a*2).asnumpy())
    	[[ 2.  2.  2.]
    	 [ 2.  2.  2.]]

    You might want to pull in data and model descriptions from locations outside the container for use by the Optimized Deep Learning Framework or save results to locations outside the container. To accomplish this, the easiest method is to mount one or more host directories as Docker data volumes.

    Note: In order to share data between ranks, NVIDIA® Collective Communications Library ™ (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:
    docker run --gpus all