📄️ Pipeline fundamentals
The main building blocks
📄️ Classic Pipeline flow
The classic Pipeline flow exposes each step directly: initialize, subscribe to output topics, publish an input tensor, run, and receive raw output tensors. It provides full control over topic wiring and tensor decoding. For most integrations, SimplifiedPipeline covers the same task with less code.
📄️ Creating ImageTensors
Image data needs to be converted into an ImageTensor before it can be published into a pipeline. An ImageTensor can be created from a file, from a memory buffer, or as a zero-filled tensor in the canonical inference format.
📄️ Custom pipeline construction
This example shows how to build a pipeline manually, adding an object detection node that uses an .denkmodel model file.
📄️ Configuration options
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📄️ Runtime parameters on exported pipelines
Exported .denkflow files from the Vision AI Hub can embed constant topic values — for example bounding-box filter thresholds (/iouthreshold, /scorethreshold) or resize dimensions (/image_size).