IBM Visual Insights runs as a small collection of pods in a Kubernetes [ref] environment. Pod is a group of containers with shared storage and network resources that are created and managed together. IBM Visual Insights stand-alone deployment version 1.2.0 used here consists of 20 Docker images. These images are used by 4 pods that provide Kubernetes infrastructure to run the
IBM Visual Insights and pods to run the actual IBM Visual Insights application.
Of course end-user does not need to be aware of these details, his entry point is just a URL to the web-based GUI through which a model can be defined and trained. Once logged into the interface, the user can upload data (images and videos, including annotated COCO data sets), label them, and train a model (classification, object detection, and action recognition models are currently supported). Example application described below will walk step-by-step through the process of training a classification model.
The hardware setup on top of which IBM Visual Insights runs consists of IBM 8335-GTH AC922 server [ref].
IBM Visual Insights (previously PowerAI Vision) makes computer vision with deep learning more accessible to business users. IBM Visual Insights includes an intuitive toolset that empowers subject matter experts to label, train, and deploy deep learning vision models, without coding or deep learning expertise. It includes the most popular deep learning frameworks and their dependencies, and it is built for easy and rapid deployment and increased team productivity. By combining IBM Visual Insights software with accelerated IBM® Power Systems™, enterprises can rapidly deploy a fully optimized and supported platform with blazing performance.