If you are doing deep learning AI research and/or development with GPUs, big chance you will be using graphics card from NVIDIA to perform the deep learning tasks. A vantage point with GPU computing is related with the fact that the graphics card occupies the PCI / PCIe slot. From the frugality point of view, it may be a brilliant idea to scavenge unused graphics cards from the fading PC world and line them up on another unused desktop motherboard to create a somewhat powerful compute node for AI tasks. Maybe not.
With the increasing popularity of container-based deployment, a system architect may consider creating several containers with each running different AI tasks. This means that that the underlying GPU resources should then be shared among the containers. NVIDIA provides a utility called NVIDIA Docker or nvidia-docker2 that enables the containerization of a GPU-accelerated machine. As the name suggests, the utility targets Docker container type. Continue reading