A Sequence Pipeline is used to treat the data and Machine Learning or Deep Learning model in a series of steps from pre-processing to model serving and post-processing on the output product. In this example, the CLI command specifies on konduit config is used to configure the configuration file to serve the models on Konduit-Serving. You'll be able to follow this example on your local terminal on any directory.
If deploying the model does not need pre- nor post-processing, only one step, a deep learning model is needed. This configuration is defined using a single Step to serve a model, and the command for creating the configuration file is like the following.
The Steps are included in the --pipeline based on the model's requirement and how output should represent. For example, the model fetches an image input, so the image_to_ndarray should be pre-processing step to convert the image into an array. The table below shows all steps that can be used in the Sequence Pipeline.
Pre-processing Step
Model/Python Step
Post-processing Step
Logging
image_to_ndarray
dl4j
keras
tensorflow
nd4jtensorflow
onnx
samediff
python
crop_grid
crop_fixed_grip
draw_bounding_box
draw_fixed_grid
draw_segmentation
extract_bounding_box
camera_frame_capture
video_frame_capture
ssd_to_bounding_box
show_image
classifier_output
logging
Here is another example with a series of step in Sequence Pipeline (image_to_ndarray to nd4jtensorflow to classifier_output). The input image needs to convert into an n-D array before feeding into the model and produce the classification output. A command likes below:
Every Step in the Pipeline needs to modify based on the input characteristics, model configurations and how output should looks like in the end. Using a configuration file allows you to serve the model with Konduit-Serving.