Core concepts

The main building blocks

Pipeline

A Pipeline is the graph that executes your complete inference workflow. You either:

• load it from a .denkflow file, or
• build it manually from .denkmodel files and utility nodes

A pipeline can be modified only before initialization.

Python

# Load from export
pipeline = Pipeline.from_denkflow("model.denkflow", pat="YOUR-PAT")

# Or build manually
pipeline = Pipeline()
pipeline.add_image_resize_node(...)

C / C++

// Load from export
DenkflowPipeline* pipeline = NULL;
denkflow_pipeline_from_denkflow(&pipeline, "model.denkflow", (void*)license_source);

Node

A Node is one processing step inside the pipeline, such as:

• image resize
• object detection
• classification
• OCR
• segmentation
• anomaly detection

Topic

A Topic is the connection name used to move tensors between producers and consumers. The common naming convention is:

node_name/output_name

Tensor

A Tensor is a payload flowing through the graph. Common tensor types include:

• ImageTensor
• BoundingBoxTensor
• ScalarTensor
• OcrTensor
• SegmentationMaskTensor
• InstanceSegmentationMaskTensor

Receiver

A Receiver subscribes to a topic and lets you wait for output data. This is how you collect inference results after calling run().

Python

receiver = pipeline.subscribe("filter/output")
# Then use the typed receive method that matches the topic's tensor type:
# receiver.receive_bounding_box_tensor()

C / C++

DenkflowReceiverTensor* receiver = NULL;
denkflow_initialized_pipeline_subscribe(&receiver, initialized_pipeline, "filter/output");

Typical evaluation workflow

Most inference scripts follow this shape:

1. Create or load a pipeline
2. Initialize the pipeline
3. Subscribe to one or more output topics
4. Publish input tensors
5. Call run()
6. Receive and decode the result

Python

pipeline = Pipeline.from_denkflow("model.denkflow", pat="PAT")
pipeline.initialize()
receiver = pipeline.subscribe("output/topic")

pipeline.publish_image_tensor("camera/image", ImageTensor.from_file("image.jpg"))
pipeline.run()

objects = receiver.receive_bounding_box_tensor().to_objects(0.5)

C / C++

denkflow_pipeline_from_denkflow(&pipeline, "model.denkflow", (void*)license_source);
denkflow_initialize_pipeline(&initialized_pipeline, &pipeline);
denkflow_initialized_pipeline_subscribe(&receiver, initialized_pipeline, "output/topic");

denkflow_image_tensor_from_file(&image_tensor, "image.jpg");
denkflow_initialized_pipeline_publish_image_tensor(initialized_pipeline, "camera/image", &image_tensor);
denkflow_initialized_pipeline_run(initialized_pipeline, 8000);

denkflow_receiver_receive_bounding_box_tensor(&tensor, receiver);
denkflow_bounding_box_tensor_to_objects(&results, tensor, 0.5f);

.denkflow versus .denkmodel

.denkflow

Use .denkflow when:

• you want the simplest deployment path
• you want export-target-specific runtime configuration
• you want quantized deployment artifacts

.denkmodel

Use .denkmodel when:

• you want to assemble the graph yourself
• you want to choose or force runtime strings like cuda, tensorrt, directml, or openvino from Python or C/C++
• you want to combine multiple models in one hand-built pipeline

Execution providers and device IDs

Each inference node runs on an execution provider (cpu, cuda, tensorrt, directml, or openvino). Custom pipelines select it via execution_provider= (Python) or the C-API equivalent; exported .denkflow files carry the runtime intent chosen at export time and can be redirected per node before initialization.

For the full provider list, device-id semantics (e.g. OpenVINO -1/-2/>=0), and override rules, see Runtime and Device Selection.

Recommended production strategy

1. Export a .denkflow file from the Vision AI Hub
2. Install the SDK for the target runtime and language
3. Keep the SDK data directory persistent (default paths are in Configuration; in Docker, mount a host volume at that default path or at the path DENKFLOW_DATA_DIRECTORY points to)
4. Deploy and benchmark on the actual target hardware