Is this the correct process for using Zingg with Spark and Delta format on Databricks?

Hi, I’ve been experimenting with zingg for the past week or two on Databricks. I just want to confirm if I’m using this correctly. I have been loading data into a dataframe and then passing that to a Zingg pipe and I specify a s3path to save as delta format for the matched output like so:

df = spark.read.load(sample_path)
inputPipe = InMemoryPipe("sample_input", df)

outputPipe = Pipe(name="sample_output", format="delta")
outputPipe.addProperty("path", output_path)

args.setData(inputPipe)
args.setOutput(outputPipe)

I run multiple rounds of findTrainingData , label and save until I have at least 30-40 matched pairs. After this I run trainMatch which will train the model and fully match the data from the inputPipe into the outputPipe. If I want to run the previously trained model on a different set of data, I have been changing the input pipe like so:

full_data_df = spark.read.load(full_data_path)
inputPipe = InMemoryPipe("sample_input", full_data_df)

and then running just match

### just run matches
options = ClientOptions([ClientOptions.PHASE, "match"])

#Zingg execution for the given phase
zingg = ZinggWithSpark(args, options)
zingg.initAndExecute()

Is this the correct process for working with Zingg?