Save the headaches, ship your dependencies
One of the biggest headaches associated with AI workloads is wrangling all of the drivers, runtimes, libraries, and other dependencies they need to run.
And because the containers are largely isolated from one another, you can usually have apps running with conflicting software stacks. For example you can have two containers, one with CUDA 11 and the other with 12, running at the same time. For example, let's say we want to make make your Intel GPU available to an Ubuntu 22.04 container. You'd appenddocker run -it --rm --device /dev/kfd --device /device/dri ubuntu:22.04To get started, we'll add the software repository for the toolkit to our sources list and refresh Apt. pipe=pipeline def kelvin_to_fahrenheit -> float: """Convert Kelvin to Fahrenheit.
On its own, the app is already fairly portable with minimal dependencies. However, it still relies on the CUDA runtime being installed correctly, something we can make easier to manage by containerizing the app.we'll define which base image we want to start with, as well as the working directory we'd like to use.to set up the container and install any dependencies. In this case, we just need a few Python modules, as well as the latest release of PyTorch for our GPU.
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