Various type of steps is available in the Pipeline of Konduit-Serving.
Konduit-Serving provides various types of steps included in Pipeline from pre-processing, serving Machine Learning or Deep Learning model and post-processing. This section offers all the configurations with descriptions that may help set your Pipeline Steps. The example of PipelineSteps:
inferenceConfiguration.pipeline(SequencePipeline.builder()
.add(new ImageToNDArrayStep() //add ImageToNDArrayStep() into pipeline to set image to NDArray for input
.config(new ImageToNDArrayConfig() //image configuration
.width(28)
.height(28)
.dataType(NDArrayType.FLOAT)
.aspectRatioHandling(AspectRatioHandling.CENTER_CROP)
.includeMinibatchDim(true)
.channelLayout(NDChannelLayout.GRAYSCALE)
.format(NDFormat.CHANNELS_FIRST)
.normalization(ImageNormalization.builder().type(ImageNormalization.Type.SCALE).build())
)
.keys("image")
.outputNames("input_layer")
.keepOtherValues(true)
.metadata(false)
.metadataKey(ImageToNDArrayStep.DEFAULT_METADATA_KEY))
.add(new Nd4jTensorFlowStep() //add Nd4jTensorFlowStep into pipeline
.modelUri(modelTrainResult.modelPath())
.inputNames(modelTrainResult.inputNames())
.outputNames(modelTrainResult.outputNames())
).add(new ClassifierOutputStep()
.inputName(modelTrainResult.outputNames().get(0))
.labels(Arrays.asList(labels.clone()))
.allProbabilities(false)
).build()
);
