Added scikit_learn_bring_your_own_model_local_serving sample code

Author: eitansela

.gitignore

@@ -69,3 +69,5 @@ pytorch_script_mode_local_model_inference/data/cifar-10-python.tar.gz
 scikit_learn_script_mode_local_serving_no_model_artifact/dummy.model
 scikit_learn_script_mode_local_serving_no_model_artifact/dummy.txt
 scikit_learn_script_mode_local_serving_no_model_artifact/model.tar.gz
+scikit_learn_bring_your_own_model_local_serving/model.joblib
+scikit_learn_bring_your_own_model_local_serving/model.tar.gz

scikit_learn_bring_your_own_model_local_serving/code/inference.py

@@ -0,0 +1,9 @@
+
+import os
+import joblib
+
+
+def model_fn(model_dir):
+    print("loading model.joblib from: {}".format(model_dir))
+    loaded_model = joblib.load(os.path.join(model_dir, "model.joblib"))
+    return loaded_model

scikit_learn_bring_your_own_model_local_serving/requirements.txt

@@ -0,0 +1,4 @@
+numpy
+pandas
+sagemaker>=2.0.0<3.0.0
+sagemaker[local]

scikit_learn_bring_your_own_model_local_serving/scikit_learn_bring_your_own_model_local_serving.py

@@ -0,0 +1,66 @@
+# This is a sample Python program that serve a scikit-learn model pre-trained on the California Housing dataset.
+# This implementation will work on your *local computer* or in the *AWS Cloud*.
+#
+# Prerequisites:
+#   1. Install required Python packages:
+#      `pip install -r requirements.txt`
+#   2. Docker Desktop installed and running on your computer:
+#      `docker ps`
+#   3. You should have AWS credentials configured on your local machine
+#      in order to be able to pull the docker image from ECR.
+###############################################################################################
+
+import boto3
+import pandas as pd
+import tarfile
+
+from sagemaker.sklearn import SKLearnModel
+from sklearn.datasets import load_boston
+from sklearn.model_selection import train_test_split
+
+DUMMY_IAM_ROLE = 'arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001'
+s3 = boto3.client('s3')
+
+
+def main():
+
+    # Prepare data for model inference - we use the Boston housing dataset
+    print('Preparing data for model inference')
+    data = load_boston()
+    X_train, X_test, y_train, y_test = train_test_split(
+        data.data, data.target, test_size=0.25, random_state=42
+    )
+
+    # we don't train a model, so we will need only the testing data
+    testX = pd.DataFrame(X_test, columns=data.feature_names)
+
+    # Download a pre-trained model file
+    print('Downloading a pre-trained model file')
+    s3.download_file('aws-ml-blog', 'artifacts/scikit_learn_bring_your_own_model/model.joblib', 'model.joblib')
+
+    # Creating a model.tar.gz file
+    tar = tarfile.open('model.tar.gz', 'w:gz')
+    tar.add('model.joblib')
+    tar.close()
+
+    model = SKLearnModel(
+        role=DUMMY_IAM_ROLE,
+        model_data='file://./model.tar.gz',
+        framework_version='0.23-1',
+        py_version='py3',
+        source_dir='code',
+        entry_point='inference.py'
+    )
+
+    print('Deploying endpoint in local mode')
+    predictor = model.deploy(initial_instance_count=1, instance_type='local')
+
+    predictions = predictor.predict(testX[data.feature_names])
+    print("Predictions: {}".format(predictor.predict(testX.values)))
+
+    print('About to delete the endpoint to stop paying (if in cloud mode).')
+    predictor.delete_endpoint(predictor.endpoint_name)
+
+
+if __name__ == "__main__":
+    main()