podman-desktop-extension-ai-lab

Podman Desktop is an open‑source graphical application that lets you manage containers and Kubernetes clusters from a single interface. It runs on Linux, macOS and Windows, so you can develop locally regardless of your operating system. The tool is positioned as a vendor‑neutral alternative that avoids lock‑in while supporting common container standards such as OCI and Docker Compose.

The main window presents a dashboard where each container appears as a card showing its name, status and basic resource usage. From there you can start, stop, pause or delete a container with a mouse click, and you can view the container's logs, environment variables and mounted volumes in side panels. When you need to build an image, the UI lets you select a Dockerfile or a Compose file, specify the build context folder, and then shows a progress bar and the resulting image ID. The output is a locally stored OCI‑compatible image that you can run, tag or push to a registry directly from the same screen.

Kubernetes functionality is layered on top of the container view. You can create a local Kind or Minikube cluster by choosing “New Cluster” and selecting the desired version; the tool then launches the necessary containers, configures the control plane and presents the cluster in a tree view. Existing remote clusters can be added by providing a kubeconfig file, after which you can browse namespaces, pods, services and deployments. Selecting a pod opens a pane with its logs, events and a terminal into the container, allowing you to debug issues without leaving the UI. Typical inputs are the kubeconfig path and optional context name; outputs are the visual representation of cluster resources and direct access to their logs.

If you work with AI models or other GPU‑heavy workloads, the GPU acceleration extension adds a checkbox in the container settings that enables the container to request access to the host's GPU devices. After enabling it, the container can run frameworks such as TensorFlow or PyTorch and expose the model's inference endpoint as a local port. The extension also reports GPU utilization in the container's resource panel.