This image contains source and binaries for NVCaffe. The pre-built and installed version of NVCaffe is located in the /usr/local/[bin,share,lib] directories. The complete source code is located in /opt/caffe directory. This container image also includes pycaffe, which makes the NVCaffe interfaces available for use through Python.

The NVIDIA ® Collective Communications Library ™ (NCCL) library and NVCaffe bindings for NCCL are installed in this container, and models using multiple GPUs will automatically leverage this library for fast parallel training. If everything is running correctly, NVCaffe should download and create a data set, and then start training LeNet. The nvidia-docker images come prepackaged, tuned, and ready to run; however, you may want to build a new image from scratch or augment an existing image with custom code, libraries, data, or settings for your corporate infrastructure. Shapefile repair tool 1000 dollar bill.

The MATLAB interface – matcaffe – is the caffe mex and its helper m-files in caffe/matlab. Load models, do forward and backward, extract output and read-only model weights, and load the binaryproto format mean as a matrix.

This section will guide you through exercises that will highlight how to create a container from scratch, customize a container, extend a deep learning framework to add features, develop some code using that extended framework from the developer environment, then package that code as a versioned release. By default, you do not need to build a container. The NGC container registry NVIDIA container repository, nvcr.io, has a number of containers that can be used immediately including containers for deep learning as well as containers with just the CUDA ® Toolkit™. One of the great things about containers is that they can be used as starting points for creating new containers. This can be referred to as customizing or extending a container. You can create a container completely from scratch, however, since these containers are likely to run on GPUs, it is recommended that you at least start with a nvcr.io container that contains the OS and CUDA ®. However, you are not limited to this and can create a container that runs on the CPUs which does not use the GPUs.

In this case, you can start with a bare OS container from another location such as Docker. To make development easier, you can still start with a container with CUDA; it is just not used when the container is used. The customized or extended containers can be saved to a user's private container repository. They can also be shared with other users but this requires some administrator help.

Attention: Do not install an NVIDIA driver into the Docker image at docker build time. The nvidia-docker is essentially a wrapper around docker that transparently provisions a container with the necessary components to execute code on the GPU. A best-practice is to avoid docker commit usage for developing new docker images, and to use Dockerfiles instead. The Dockerfile method provides visibility and capability to efficiently version-control changes made during development of a Docker image. The Docker commit method is appropriate for short-lived, disposable images only.