Force version of sklearn in examples

sdks/python/apache_beam/examples/inference/README.md

@@ -334,6 +334,7 @@ To use this transform, you need a dataset and model for language modeling.
 ...
 ```
 2. Create a file named `MODEL_PATH` that contains the pickled file of a scikit-learn model trained on MNIST data. Please refer to this scikit-learn [model persistence documentation](https://scikit-learn.org/stable/model_persistence.html) on how to serialize models.
+3. Update sklearn_examples_requirements.txt to match the version of sklearn used to train the model. Sklearn doesn't guarantee model compatability between versions.
 
 
 ### Running `sklearn_mnist_classification.py`

sdks/python/apache_beam/examples/inference/sklearn_examples_requirements.txt

@@ -0,0 +1,20 @@
+#
+# 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.
+#
+
+# This should match the saved version of your trained model.
+# Beam's tests use sklearn 1.0.2 for their saved models.
+scikit-learn==1.0.2

sdks/python/apache_beam/examples/inference/sklearn_japanese_housing_regression.py

@@ -30,6 +30,7 @@
 """
 
 import argparse
+import os
 from typing import Iterable
 
 import pandas
@@ -137,6 +138,12 @@ def run(
   known_args, pipeline_args = parse_known_args(argv)
   pipeline_options = PipelineOptions(pipeline_args)
   pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+  requirements_dir = os.path.dirname(os.path.realpath(__file__))
+  # Pin to the version that we trained the model on.
+  # Sklearn doesn't guarantee compatability between versions.
+  pipeline_options.view_as(
+      SetupOptions
+  ).requirements_file = f'{requirements_dir}/sklearn_examples_requirements.txt'
 
   pipeline = test_pipeline
   if not test_pipeline:

sdks/python/apache_beam/examples/inference/sklearn_mnist_classification.py

@@ -26,6 +26,7 @@
 
 import argparse
 import logging
+import os
 from typing import Iterable
 from typing import List
 from typing import Tuple
@@ -90,6 +91,12 @@ def run(
   known_args, pipeline_args = parse_known_args(argv)
   pipeline_options = PipelineOptions(pipeline_args)
   pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
+  requirements_dir = os.path.dirname(os.path.realpath(__file__))
+  # Pin to the version that we trained the model on.
+  # Sklearn doesn't guarantee compatability between versions.
+  pipeline_options.view_as(
+      SetupOptions
+  ).requirements_file = f'{requirements_dir}/sklearn_examples_requirements.txt'
 
   # In this example we pass keyed inputs to RunInference transform.
   # Therefore, we use KeyedModelHandler wrapper over SklearnModelHandlerNumpy.