Standard Transformer UPI

This guide assumes you have experience using standard transformer and are familiar with UPI contract. You can refer to to get details on the contract.

There are 2 key differences in Standard Transformer when it’s deployed using UPI protocol:

  1. Autoload Feature

  2. Separate Output Operation for Pre-process and Post-process

Autoload Feature

Autoload feature is the primary mechanism for importing values in the request payload as variables or tables into standard transformer. Previously, it is done by using JSONPath query in the HTTP mode.

For example in HTTP model, if you want to declare a rating variable that should use value from user_rating field of the below incoming request

  "user_id": 12345,
  "user_rating": 4.9,
  "user_name": "jon_doe"

then you have to declare following configuration in the input configuration of the standard transformer which will extract the data from incoming request. The drawback of this approach is that it could be complicated for a more complex request payload and for large number of variable/table to be imported.

  - variables:
      - name: rating
          jsonPath: $.user_rating
          defaultValue: -1
          valueType: FLOAT

You can avoid it altogether by using autoload feature in UPI. To do so:

Store the variable/table in prediction_table or transformer_input field of the PredictValuesRequest

For example, when using Python SDK, you can do so by following code. In below example, we are storing user_rating as variable and customer_df as customer_table in transformer_input, as well as sending the prediction_df as prediction_table.
from caraml.upi.v1 import type_pb2, upi_pb2_grpc, upi_pb2, variable_pb2

request = upi_pb2.PredictValuesRequest(
    # ...
    prediction_table=df_to_table(predict_df, "prediction_table"),
        tables=[df_to_table(customer_df, "customer_table")]
    # ...

Add autoload feature in the standard transformer config.

Add all variables and tables that are going to be imported in the standard transformer. In below example we are importing prediction_table, customer_table , and user_rating that was sent by the client.

Which will add following config

      - autoload:
            - customer_table
            - user_rating
  postprocess: {}

Note that, the table created using UPI autoload will have additional column row_id which will store the row_ids value of the associated table. The imported tables and variables from UPI Autoload can then be used for downstream transformation in the standard transformer’s preprocess and post-process.

Preprocess & Postprocess Output

Standard transformer’s preprocessing output in UPI mode must satisfy PredictValuesRequest structure. You can populate prediction_table field and tables in transformer_inputs fields of the PredictValuesRequest that will be sent to model by defining its source tables. The source tables must be tables that have been declared in preprocessing pipeline.

Example below shows a preprocessing pipeline which join prediction_table and sample_table to produce preprocessed_table , and then use the preprocessed_table as the prediction_table of the PredictValuesRequest that is sent to model.

      - autoload:
            - prediction_table
            - sample_table
      - tableJoin:
          leftTable: prediction_table
          rightTable: sample_table
          outputTable: preprocessed_table
          how: LEFT
            - row_id
      - upiPreprocessOutput:
          predictionTableName: preprocessed_table
          transformerInputTableNames: []
  postprocess: {}

Similarly, post-processing output in UPI mode must satisfy PredictValuesResponse. You can populate prediction_result_table of the PredictValuesResponse that will be sent back to client by defining its source table. The source table can be a table declared both in preprocessing and post-processing pipeline.

When pre-processing or post-processing pipeline is not defined, standard transformer will simply forward the request/response to its receiver.

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