Use RunInference with Sklearn
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The following examples demonstrate how to to create pipelines that use the Beam RunInference API and Sklearn.
Example 1: Sklearn unkeyed model
In this example, we create a pipeline that uses an SKlearn RunInference transform on unkeyed data.
import apache_beam as beam
import numpy
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
sklearn_model_filename = 'gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl' # pylint: disable=line-too-long
sklearn_model_handler = SklearnModelHandlerNumpy(
model_uri=sklearn_model_filename, model_file_type=ModelFileType.PICKLE)
unkeyed_data = numpy.array([20, 40, 60, 90],
dtype=numpy.float32).reshape(-1, 1)
with beam.Pipeline() as p:
predictions = (
p
| "ReadInputs" >> beam.Create(unkeyed_data)
| "RunInferenceSklearn" >>
RunInference(model_handler=sklearn_model_handler)
| beam.Map(print))
Output:
PredictionResult(example=array([20.], dtype=float32), inference=array([100.], dtype=float32), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl')
PredictionResult(example=array([40.], dtype=float32), inference=array([200.], dtype=float32), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl')
PredictionResult(example=array([60.], dtype=float32), inference=array([300.], dtype=float32), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl')
PredictionResult(example=array([90.], dtype=float32), inference=array([450.], dtype=float32), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl')
Example 2: Sklearn keyed model
In this example, we create a pipeline that uses an SKlearn RunInference transform on keyed data.
import apache_beam as beam
from apache_beam.ml.inference.base import KeyedModelHandler
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
sklearn_model_filename = 'gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl' # pylint: disable=line-too-long
sklearn_model_handler = KeyedModelHandler(
SklearnModelHandlerNumpy(
model_uri=sklearn_model_filename,
model_file_type=ModelFileType.PICKLE))
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
| "ReadInputs" >> beam.Create(keyed_data)
| "ConvertDataToList" >> beam.Map(lambda x: (x[0], [x[1]]))
| "RunInferenceSklearn" >>
RunInference(model_handler=sklearn_model_handler)
| beam.Map(print))
Output:
('first_question', PredictionResult(example=[105.0], inference=array([525.]), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl'))
('second_question', PredictionResult(example=[108.0], inference=array([540.]), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl'))
('third_question', PredictionResult(example=[1000.0], inference=array([5000.]), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl'))
('fourth_question', PredictionResult(example=[1013.0], inference=array([5065.]), model_id='gs://apache-beam-samples/run_inference/five_times_table_sklearn.pkl'))
Last updated on 2024/10/06
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