yeandy commented on code in PR #21781: URL: https://github.com/apache/beam/pull/21781#discussion_r898056281
########## sdks/python/apache_beam/examples/inference/sklearn_mnist_classification.py: ########## @@ -0,0 +1,112 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""A pipeline that uses RunInference API to classify MNIST data. + +This pipeline takes a text file in which data is comma separated ints. The first +column would be the true label and the rest would be the pixel values. The data +is processed and then a model trained on the MNIST data would be used to perform +the inference. The pipeline writes the prediction to an output file in which +users can then compare against the true label. +""" + +import argparse +from typing import Iterable +from typing import List +from typing import Tuple + +import apache_beam as beam +from apache_beam.ml.inference.base import KeyedModelHandler +from apache_beam.ml.inference.base import PredictionResult +from apache_beam.ml.inference.base import RunInference +from apache_beam.ml.inference.sklearn_inference import ModelFileType +from apache_beam.ml.inference.sklearn_inference import SklearnModelHandlerNumpy +from apache_beam.options.pipeline_options import PipelineOptions +from apache_beam.options.pipeline_options import SetupOptions + + +def process_input(row: str) -> Tuple[int, List[int]]: + data = row.split(',') + label, pixels = int(data[0]), data[1:] + pixels = [int(pixel) for pixel in pixels] + return label, pixels + + +class PostProcessor(beam.DoFn): + """Process the PredictionResult to get the predicted label. + Returns a comma separated string with true label and predicted label. + """ + def process(self, element: Tuple[int, PredictionResult]) -> Iterable[str]: + label, prediction_result = element + prediction = prediction_result.inference + yield '{},{}'.format(label, prediction) + + +def parse_known_args(argv): + """Parses args for the workflow.""" + parser = argparse.ArgumentParser() + parser.add_argument( + '--input_file', + dest='input', + required=True, + help='CSV file with row containing label and pixel values.') + parser.add_argument( + '--output', + dest='output', + required=True, + help='Path to save output predictions.') + parser.add_argument( + '--model_path', + dest='model_path', + required=True, + help='Path to load the Sklearn model for Inference.') + return parser.parse_known_args(argv) + + +def run(argv=None, save_main_session=True): + """Entry point. Defines and runs the pipeline.""" + known_args, pipeline_args = parse_known_args(argv) + pipeline_options = PipelineOptions(pipeline_args) + pipeline_options.view_as(SetupOptions).save_main_session = save_main_session + + # In this example we pass keyed inputs to RunInference transform. + # Therefore, we use KeyedModelHandler wrapper over SklearnModelHandlerNumpy. + model_loader = KeyedModelHandler( + SklearnModelHandlerNumpy( + model_file_type=ModelFileType.PICKLE, + model_uri=known_args.model_path)) + + with beam.Pipeline(options=pipeline_options) as p: + label_pixel_tuple = ( + p + | "ReadFromInput" >> beam.io.ReadFromText( + known_args.input, skip_header_lines=1) + | "PreProcessInputs" >> beam.Map(process_input)) + + predictions = ( + label_pixel_tuple + | "RunInference" >> RunInference(model_loader) + | "PostProcessOutputs" >> beam.ParDo(PostProcessor())) + + _ = predictions | "WriteOutputToGCS" >> beam.io.WriteToText( Review Comment: ```suggestion _ = predictions | "WriteOutput" >> beam.io.WriteToText( ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
