TheNeuralBit commented on code in PR #17368: URL: https://github.com/apache/beam/pull/17368#discussion_r856490682
########## sdks/python/apache_beam/ml/inference/sklearn_loader.py: ########## @@ -0,0 +1,78 @@ +# +# 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. +# + +import enum +import pickle +import sys +from typing import Any +from typing import Iterable +from typing import List + +import numpy + +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.api import PredictionResult +from apache_beam.ml.inference.base import InferenceRunner +from apache_beam.ml.inference.base import ModelLoader + +try: + import joblib +except ImportError: + # joblib is an optional dependency. + pass + + +class ModelFileType(enum.Enum): + PICKLE = 1 + JOBLIB = 2 + + +class SklearnInferenceRunner(InferenceRunner): + def run_inference(self, batch: List[numpy.array], + model: Any) -> Iterable[numpy.array]: + # vectorize data for better performance + vectorized_batch = numpy.stack(batch, axis=0) + predictions = model.predict(vectorized_batch) + return [PredictionResult(x, y) for x, y in zip(batch, predictions)] + + def get_num_bytes(self, batch: List[numpy.array]) -> int: + """Returns the number of bytes of data for a batch.""" + return sum(sys.getsizeof(element) for element in batch) + + +class SklearnModelLoader(ModelLoader): + def __init__( + self, + model_file_type: ModelFileType = ModelFileType.PICKLE, + model_uri: str = ''): + self._model_file_type = model_file_type + self._model_uri = model_uri + self._inference_runner = SklearnInferenceRunner() + + def load_model(self): + """Loads and initializes a model for processing.""" + file = FileSystems.open(self._model_uri, 'rb') + if self._model_file_type == ModelFileType.PICKLE: + return pickle.load(file) + elif self._model_file_type == ModelFileType.JOBLIB: + if not joblib: + raise ImportError('Joblib not available in SklearnModelLoader.') Review Comment: Note this will be an execution time error causing workers to crash, which isn't a great experience. You might consider pointing this to some documentation about setting up dependencies. ########## sdks/python/setup.py: ########## @@ -169,6 +170,7 @@ def get_version(): 'pytest>=4.4.0,<5.0', 'pytest-xdist>=1.29.0,<2', 'pytest-timeout>=1.3.3,<2', + 'scikit-learn>=0.24.2', Review Comment: It would be good to get an answer on the above (and similarly for joblib) ########## sdks/python/apache_beam/ml/inference/sklearn_loader.py: ########## @@ -0,0 +1,78 @@ +# +# 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. +# + +import enum +import pickle +import sys +from typing import Any +from typing import Iterable +from typing import List + +import numpy + +from apache_beam.io.filesystems import FileSystems +from apache_beam.ml.inference.api import PredictionResult +from apache_beam.ml.inference.base import InferenceRunner +from apache_beam.ml.inference.base import ModelLoader + +try: + import joblib +except ImportError: + # joblib is an optional dependency. + pass + + +class ModelFileType(enum.Enum): + PICKLE = 1 + JOBLIB = 2 + + +class SklearnInferenceRunner(InferenceRunner): + def run_inference(self, batch: List[numpy.array], + model: Any) -> Iterable[numpy.array]: + # vectorize data for better performance + vectorized_batch = numpy.stack(batch, axis=0) + predictions = model.predict(vectorized_batch) + return [PredictionResult(x, y) for x, y in zip(batch, predictions)] + + def get_num_bytes(self, batch: List[numpy.array]) -> int: + """Returns the number of bytes of data for a batch.""" + return sum(sys.getsizeof(element) for element in batch) + + +class SklearnModelLoader(ModelLoader): + def __init__( + self, + model_file_type: ModelFileType = ModelFileType.PICKLE, + model_uri: str = ''): + self._model_file_type = model_file_type + self._model_uri = model_uri + self._inference_runner = SklearnInferenceRunner() + + def load_model(self): + """Loads and initializes a model for processing.""" + file = FileSystems.open(self._model_uri, 'rb') + if self._model_file_type == ModelFileType.PICKLE: + return pickle.load(file) + elif self._model_file_type == ModelFileType.JOBLIB: + if not joblib: + raise ImportError('Joblib not available in SklearnModelLoader.') + return joblib.load(file) + raise TypeError('Unsupported serialization type.') Review Comment: nit: I might make this an assertion error since it's a state we shouldn't get to given ModelFileType is an enum. -- 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]
