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.
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, can be specified using
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.
- 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 nvcr.io/nvidia/theano:18.08
- 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:
docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/theano:<xx.xx>
Theano is run by importing it as a Python module:
$ python >>> import numpy >>> import theano.tensor as T >>> from theano import function >>> x = T.dscalar('x') >>> y = T.dscalar('y') >>> z = x + y >>> f = function([x, y], z) >>> f(2, 3) array(5.0) >>> numpy.allclose(f(16.3, 12.1), 28.4) True
You might want to pull in data and model descriptions from locations outside the container for use by Theano 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.