Add test for BYO estimator

tests/integ/test_byo_estimator.py

@@ -0,0 +1,99 @@
+# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file 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 gzip
+import io
+import json
+import numpy as np
+import os
+import pickle
+import sys
+
+import boto3
+
+import sagemaker
+from sagemaker.estimator import Estimator
+from sagemaker.amazon.amazon_estimator import registry
+from sagemaker.amazon.common import write_numpy_to_dense_tensor
+from sagemaker.utils import name_from_base
+from tests.integ import DATA_DIR, REGION
+from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
+
+
+def test_byo_estimator():
+    """Use Factorization Machines algorithm as an example here.
+
+    First we need to prepare data for training. We take standard data set, convert it to the
+    format that the algorithm can process and upload it to S3.
+    Then we create the Estimator and set hyperparamets as required by the algorithm.
+    Next, we can call fit() with path to the S3.
+    Later the trained model is deployed and prediction is called against the endpoint.
+    Default predictor is updated with json serializer and deserializer.
+
+    """
+    image_name = registry(REGION) + "/factorization-machines:1"
+
+    with timeout(minutes=15):
+        sagemaker_session = sagemaker.Session(boto_session=boto3.Session(region_name=REGION))
+        data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
+        pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
+
+        with gzip.open(data_path, 'rb') as f:
+            train_set, _, _ = pickle.load(f, **pickle_args)
+
+        # take 100 examples for faster execution
+        vectors = np.array([t.tolist() for t in train_set[0][:100]]).astype('float32')
+        labels = np.where(np.array([t.tolist() for t in train_set[1][:100]]) == 0, 1.0, 0.0).astype('float32')
+
+        buf = io.BytesIO()
+        write_numpy_to_dense_tensor(buf, vectors, labels)
+        buf.seek(0)
+
+        bucket = sagemaker_session.default_bucket()
+        prefix = 'test_byo_estimator'
+        key = 'recordio-pb-data'
+        boto3.resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'train', key)).upload_fileobj(buf)
+        s3_train_data = 's3://{}/{}/train/{}'.format(bucket, prefix, key)
+
+        estimator = Estimator(image_name=image_name,
+                              role='SageMakerRole', train_instance_count=1,
+                              train_instance_type='ml.c4.xlarge',
+                              sagemaker_session=sagemaker_session, base_job_name='test-byo')
+
+        estimator.set_hyperparameters(num_factors=10,
+                                      feature_dim=784,
+                                      mini_batch_size=100,
+                                      predictor_type='binary_classifier')
+
+        # training labels must be 'float32'
+        estimator.fit({'train': s3_train_data})
+
+    endpoint_name = name_from_base('byo')
+
+    def fm_serializer(data):
+        js = {'instances': []}
+        for row in data:
+            js['instances'].append({'features': row.tolist()})
+        return json.dumps(js)
+
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+        model = estimator.create_model()
+        predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
+        predictor.serializer = fm_serializer
+        predictor.content_type = 'application/json'
+        predictor.deserializer = sagemaker.predictor.json_deserializer
+
+        result = predictor.predict(train_set[0][:10])
+
+        assert len(result['predictions']) == 10
+        for prediction in result['predictions']:
+            assert prediction['score'] is not None