yeandy commented on code in PR #17368:
URL: https://github.com/apache/beam/pull/17368#discussion_r851304959
##########
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:
How does this affect `scikit-learn` in the
[base_image_requirements_manual.txt](https://github.com/apache/beam/blob/master/sdks/python/container/base_image_requirements_manual.txt#L42)?
##########
sdks/python/apache_beam/ml/inference/sklearn_loader.py:
##########
@@ -0,0 +1,73 @@
+#
+# 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 abc
+import enum
+import pickle
+import sys
+from dataclasses import dataclass
+from typing import Any
+from typing import Iterable
+from typing import List
+
+import joblib
+import numpy
+
+import apache_beam.ml.inference.api as api
+import apache_beam.ml.inference.base as base
+import sklearn_loader
+from apache_beam.io.filesystems import FileSystems
+
+
+class SerializationType(enum.Enum):
+ PICKLE = 1
+ JOBLIB = 2
+
+
+class SKLearnInferenceRunner(base.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 [api.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(base.ModelLoader):
+ def __init__(
+ self,
+ serialization: SerializationType = SerializationType.PICKLE,
Review Comment:
```suggestion
serialization_type: SerializationType = SerializationType.PICKLE,
```
Would it be more clear to have `serialization_type`?
##########
sdks/python/apache_beam/ml/inference/sklearn_loader.py:
##########
@@ -0,0 +1,73 @@
+#
+# 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 abc
+import enum
+import pickle
+import sys
+from dataclasses import dataclass
+from typing import Any
+from typing import Iterable
+from typing import List
+
+import joblib
+import numpy
+
+import apache_beam.ml.inference.api as api
+import apache_beam.ml.inference.base as base
+import sklearn_loader
+from apache_beam.io.filesystems import FileSystems
+
+
+class SerializationType(enum.Enum):
+ PICKLE = 1
+ JOBLIB = 2
+
+
+class SKLearnInferenceRunner(base.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 [api.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)
Review Comment:
Does `sys.getsizeof(element)` return the size in bytes of all features in
the `element` numpy array? i.e. if each `element` has 4 numeric features, with
each feature as 4 bytes, then it will return 16? And then if we have 2
`elements` in the `batch`, then we will return 32?
##########
sdks/python/setup.py:
##########
@@ -159,6 +159,7 @@ def get_version():
REQUIRED_TEST_PACKAGES = [
'freezegun>=0.3.12',
+ 'joblib>=1.1.0',
Review Comment:
Should we have `joblib` be in the `REQUIRED_PACKAGES`? technically it's
being used in the regular `sklearn_loader.py` file.
##########
sdks/python/apache_beam/ml/inference/sklearn_loader.py:
##########
@@ -0,0 +1,73 @@
+#
+# 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 abc
+import enum
+import pickle
+import sys
+from dataclasses import dataclass
+from typing import Any
+from typing import Iterable
+from typing import List
+
+import joblib
+import numpy
+
+import apache_beam.ml.inference.api as api
+import apache_beam.ml.inference.base as base
+import sklearn_loader
+from apache_beam.io.filesystems import FileSystems
+
+
+class SerializationType(enum.Enum):
+ PICKLE = 1
+ JOBLIB = 2
+
+
+class SKLearnInferenceRunner(base.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 [api.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(base.ModelLoader):
+ def __init__(
+ self,
+ serialization: SerializationType = SerializationType.PICKLE,
+ model_uri: str = ''):
+ self._serialization = serialization
+ self._model_uri = model_uri
Review Comment:
Should we try to be as consistent as possible across frameworks? For
example, for Pytorch, I use `state_dict_path`, but I could change it to be
`state_dict_uri`.
##########
sdks/python/apache_beam/ml/inference/sklearn_loader.py:
##########
@@ -0,0 +1,73 @@
+#
+# 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 abc
+import enum
+import pickle
+import sys
+from dataclasses import dataclass
+from typing import Any
+from typing import Iterable
+from typing import List
+
+import joblib
+import numpy
+
+import apache_beam.ml.inference.api as api
+import apache_beam.ml.inference.base as base
+import sklearn_loader
+from apache_beam.io.filesystems import FileSystems
+
+
+class SerializationType(enum.Enum):
+ PICKLE = 1
+ JOBLIB = 2
+
+
+class SKLearnInferenceRunner(base.InferenceRunner):
Review Comment:
Silly question, but for the sake of consistency and ease of use, what should
the naming convention be for the different frameworks? For example, technically
`PyTorch` has capital P and T, but in my implementation, I use `Pytorch` for
simplicity. (I can change it though)
And for `Scikit-learn`, it's often abbreviated as `sklearn` or `Sklearn`,
without the capital K or L. Should we change it to `SklearnInferenceRunner`?
##########
sdks/python/apache_beam/ml/inference/sklearn_loader.py:
##########
@@ -0,0 +1,73 @@
+#
+# 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 abc
+import enum
+import pickle
+import sys
+from dataclasses import dataclass
+from typing import Any
+from typing import Iterable
+from typing import List
+
+import joblib
+import numpy
+
+import apache_beam.ml.inference.api as api
+import apache_beam.ml.inference.base as base
+import sklearn_loader
+from apache_beam.io.filesystems import FileSystems
+
+
+class SerializationType(enum.Enum):
+ PICKLE = 1
+ JOBLIB = 2
+
+
+class SKLearnInferenceRunner(base.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 [api.PredictionResult(x, y) for x, y in zip(batch, predictions)]
Review Comment:
Nice!
##########
sdks/python/apache_beam/ml/inference/sklearn_loader.py:
##########
@@ -0,0 +1,73 @@
+#
+# 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 abc
+import enum
+import pickle
+import sys
+from dataclasses import dataclass
+from typing import Any
+from typing import Iterable
+from typing import List
+
+import joblib
+import numpy
+
+import apache_beam.ml.inference.api as api
+import apache_beam.ml.inference.base as base
+import sklearn_loader
+from apache_beam.io.filesystems import FileSystems
+
+
+class SerializationType(enum.Enum):
+ PICKLE = 1
+ JOBLIB = 2
+
+
+class SKLearnInferenceRunner(base.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 [api.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(base.ModelLoader):
+ def __init__(
+ self,
+ serialization: SerializationType = SerializationType.PICKLE,
+ model_uri: str = ''):
+ self._serialization = serialization
+ 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._serialization == SerializationType.PICKLE:
+ return pickle.load(file)
+ elif self._serialization == SerializationType.JOBLIB:
+ return joblib.load(file)
+ raise ValueError('No supported serialization type.')
Review Comment:
Assuming the user is only picking from the `SerializationType` enums (and
using type checking), we will never hit this case, right?
Can we add in the error message the value of `self._serialization`? And also
add a test for this?
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