Pipeline Steps
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: 1
inferenceConfiguration.pipeline(SequencePipeline.builder()
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.add(new ImageToNDArrayStep() //add ImageToNDArrayStep() into pipeline to set image to NDArray for input
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.config(new ImageToNDArrayConfig() //image configuration
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.width(28)
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.height(28)
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.dataType(NDArrayType.FLOAT)
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.aspectRatioHandling(AspectRatioHandling.CENTER_CROP)
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.includeMinibatchDim(true)
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.channelLayout(NDChannelLayout.GRAYSCALE)
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.format(NDFormat.CHANNELS_FIRST)
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.normalization(ImageNormalization.builder().type(ImageNormalization.Type.SCALE).build())
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)
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.keys("image")
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.outputNames("input_layer")
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.keepOtherValues(true)
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.metadata(false)
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.metadataKey(ImageToNDArrayStep.DEFAULT_METADATA_KEY))
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.add(new Nd4jTensorFlowStep() //add Nd4jTensorFlowStep into pipeline
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.modelUri(modelTrainResult.modelPath())
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.inputNames(modelTrainResult.inputNames())
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.outputNames(modelTrainResult.outputNames())
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).add(new ClassifierOutputStep()
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.inputName(modelTrainResult.outputNames().get(0))
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.labels(Arrays.asList(labels.clone()))
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.allProbabilities(false)
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).build()
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);
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