Use RunInference with PyTorch

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The following examples demonstrate how to create pipelines that use the Beam RunInference API and PyTorch.

Example 1: PyTorch unkeyed model

In this example, we create a pipeline that uses a PyTorch RunInference transform on unkeyed data.

import apache_beam as beam import numpy import torch from apache_beam.ml.inference.base import RunInference from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor  model_state_dict_path = 'gs://apache-beam-samples/run_inference/five_times_table_torch.pt' # pylint: disable=line-too-long model_class = LinearRegression model_params = {'input_dim': 1, 'output_dim': 1} model_handler = PytorchModelHandlerTensor(  model_class=model_class,  model_params=model_params,  state_dict_path=model_state_dict_path)  unkeyed_data = numpy.array([10, 40, 60, 90],  dtype=numpy.float32).reshape(-1, 1)  with beam.Pipeline() as p:  predictions = (  p  | 'InputData' >> beam.Create(unkeyed_data)  | 'ConvertNumpyToTensor' >> beam.Map(torch.Tensor)  | 'PytorchRunInference' >> RunInference(model_handler=model_handler)  | beam.Map(print))

Output:

PredictionResult(example=tensor([10.]), inference=tensor([52.2325]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt') PredictionResult(example=tensor([40.]), inference=tensor([201.1165]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt') PredictionResult(example=tensor([60.]), inference=tensor([300.3724]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt') PredictionResult(example=tensor([90.]), inference=tensor([449.2563]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt')

Example 2: PyTorch keyed model

In this example, we create a pipeline that uses a PyTorch RunInference transform on keyed data.

import apache_beam as beam import torch from apache_beam.ml.inference.base import KeyedModelHandler from apache_beam.ml.inference.base import RunInference from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor  model_state_dict_path = 'gs://apache-beam-samples/run_inference/five_times_table_torch.pt' # pylint: disable=line-too-long model_class = LinearRegression model_params = {'input_dim': 1, 'output_dim': 1} keyed_model_handler = KeyedModelHandler(  PytorchModelHandlerTensor(  model_class=model_class,  model_params=model_params,  state_dict_path=model_state_dict_path))  keyed_data = [("first_question", 105.00), ("second_question", 108.00),  ("third_question", 1000.00), ("fourth_question", 1013.00)]  with beam.Pipeline() as p:  predictions = (  p  | 'KeyedInputData' >> beam.Create(keyed_data)  | "ConvertIntToTensor" >>  beam.Map(lambda x: (x[0], torch.Tensor([x[1]])))  | 'PytorchRunInference' >>  RunInference(model_handler=keyed_model_handler)  | beam.Map(print))

Output:

('first_question', PredictionResult(example=tensor([105.]), inference=tensor([523.6982]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt')) ('second_question', PredictionResult(example=tensor([108.]), inference=tensor([538.5867]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt')) ('third_question', PredictionResult(example=tensor([1000.]), inference=tensor([4965.4019]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt')) ('fourth_question', PredictionResult(example=tensor([1013.]), inference=tensor([5029.9180]), model_id='gs://apache-beam-samples/run_inference/five_times_table_torch.pt'))