simplify create_image_uri function

src/sagemaker/fw_utils.py

@@ -33,8 +33,10 @@
                                   'Please add framework_version={} to your constructor to avoid ' \
                                   'an error in the future.'
 
+VALID_PY_VERSIONS = ['py2', 'py3']
 
-def create_image_uri(region, framework, instance_type, framework_version, py_version, account='520713654638',
+
+def create_image_uri(region, framework, instance_type, framework_version, py_version=None, account='520713654638',
                      optimized_families=[]):
     """Return the ECR URI of an image.
 
@@ -43,14 +45,18 @@ def create_image_uri(region, framework, instance_type, framework_version, py_ver
         framework (str): framework used by the image.
         instance_type (str): SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).
         framework_version (str): The version of the framework.
-        py_version (str): Python version. One of 'py2' or 'py3'.
+        py_version (str): Optional. Python version. If specified, should be one of 'py2' or 'py3'.
+            If not specified, image uri will not include a python component.
         account (str): AWS account that contains the image. (default: '520713654638')
         optimized_families (str): Instance families for which there exist specific optimized images.
 
     Returns:
         str: The appropriate image URI based on the given parameters.
     """
 
+    if py_version and py_version not in VALID_PY_VERSIONS:
+        raise ValueError('invalid py_version argument: {}'.format(py_version))
+
     # Handle Account Number for Gov Cloud
     if region == 'us-gov-west-1':
         account = '246785580436'
@@ -73,7 +79,10 @@ def create_image_uri(region, framework, instance_type, framework_version, py_ver
         else:
             device_type = 'cpu'
 
-    tag = "{}-{}-{}".format(framework_version, device_type, py_version)
+    if py_version:
+        tag = "{}-{}-{}".format(framework_version, device_type, py_version)
+    else:
+        tag = "{}-{}".format(framework_version, device_type)
     return "{}.dkr.ecr.{}.amazonaws.com/sagemaker-{}:{}" \
         .format(account, region, framework, tag)
 

src/sagemaker/tensorflow/serving.py

@@ -134,7 +134,7 @@ def _get_container_env(self):
             return self.env
 
         env = dict(self.env)
-        env['SAGEMAKER_TFS_NGINX_LOGLEVEL'] = Model.LOG_LEVEL_MAP[self._container_log_level]
+        env[Model.LOG_LEVEL_PARAM_NAME] = Model.LOG_LEVEL_MAP[self._container_log_level]
         return env
 
     def _get_image_uri(self, instance_type):
@@ -143,7 +143,5 @@ def _get_image_uri(self, instance_type):
 
         # reuse standard image uri function, then strip unwanted python component
         region_name = self.sagemaker_session.boto_region_name
-        image = create_image_uri(region_name, Model.FRAMEWORK_NAME, instance_type,
-                                 self._framework_version, 'py3')
-        image = image.replace('-py3', '')
-        return image
+        return create_image_uri(region_name, Model.FRAMEWORK_NAME, instance_type,
+                                self._framework_version)

tests/integ/test_tfs.py

@@ -12,6 +12,7 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import botocore.exceptions
 import pytest
 
 import sagemaker
@@ -29,8 +30,9 @@ def instance_type(request):
 @pytest.fixture(scope='module')
 def tfs_predictor(instance_type, sagemaker_session, tf_full_version):
     endpoint_name = sagemaker.utils.name_from_base('sagemaker-tensorflow-serving')
-    model_data = sagemaker_session.upload_data(path='tests/data/tensorflow-serving-test-model.tar.gz',
-                                               key_prefix='tensorflow-serving/models')
+    model_data = sagemaker_session.upload_data(
+        path='tests/data/tensorflow-serving-test-model.tar.gz',
+        key_prefix='tensorflow-serving/models')
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         model = Model(model_data=model_data, role='SageMakerRole',
                       framework_version=tf_full_version,
@@ -80,3 +82,9 @@ def test_predict_csv(tfs_predictor):
 
     result = predictor.predict(input_data)
     assert expected_result == result
+
+
+def test_predict_bad_input(tfs_predictor):
+    input_data = {'junk': 'data'}
+    with pytest.raises(botocore.exceptions.ClientError):
+        tfs_predictor.predict(input_data)

tests/unit/test_fw_utils.py

@@ -13,13 +13,15 @@
 from __future__ import absolute_import
 
 import inspect
-from mock import Mock, patch
 import os
-from sagemaker.fw_utils import create_image_uri, framework_name_from_image, framework_version_from_tag, \
-    model_code_key_prefix
-from sagemaker.fw_utils import tar_and_upload_dir, parse_s3_url, UploadedCode, validate_source_dir
+
 import pytest
+from mock import Mock, patch
 
+from sagemaker.fw_utils import create_image_uri, framework_name_from_image, \
+    framework_version_from_tag, \
+    model_code_key_prefix
+from sagemaker.fw_utils import tar_and_upload_dir, parse_s3_url, UploadedCode, validate_source_dir
 from sagemaker.utils import name_from_image
 
 DATA_DIR = 'data_dir'
@@ -33,12 +35,12 @@
 @pytest.fixture()
 def sagemaker_session():
     boto_mock = Mock(name='boto_session', region_name=REGION)
-    ims = Mock(name='sagemaker_session', boto_session=boto_mock)
-    ims.default_bucket = Mock(name='default_bucket', return_value=BUCKET_NAME)
-    ims.expand_role = Mock(name="expand_role", return_value=ROLE)
-    ims.sagemaker_client.describe_training_job = Mock(return_value={'ModelArtifacts':
-                                                                    {'S3ModelArtifacts': 's3://m/m.tar.gz'}})
-    return ims
+    session_mock = Mock(name='sagemaker_session', boto_session=boto_mock)
+    session_mock.default_bucket = Mock(name='default_bucket', return_value=BUCKET_NAME)
+    session_mock.expand_role = Mock(name="expand_role", return_value=ROLE)
+    session_mock.sagemaker_client.describe_training_job = \
+        Mock(return_value={'ModelArtifacts': {'S3ModelArtifacts': 's3://m/m.tar.gz'}})
+    return session_mock
 
 
 def test_create_image_uri_cpu():
@@ -49,6 +51,16 @@ def test_create_image_uri_cpu():
     assert image_uri == '23.dkr.ecr.mars-south-3.amazonaws.com/sagemaker-mlfw:1.0rc-cpu-py2'
 
 
+def test_create_image_uri_no_python():
+    image_uri = create_image_uri('mars-south-3', 'mlfw', 'ml.c4.large', '1.0rc', account='23')
+    assert image_uri == '23.dkr.ecr.mars-south-3.amazonaws.com/sagemaker-mlfw:1.0rc-cpu'
+
+
+def test_create_image_uri_bad_python():
+    with pytest.raises(ValueError):
+        create_image_uri('mars-south-3', 'mlfw', 'ml.c4.large', '1.0rc', 'py0')
+
+
 def test_create_image_uri_gpu():
     image_uri = create_image_uri('mars-south-3', 'mlfw', 'ml.p3.2xlarge', '1.0rc', 'py3', '23')
     assert image_uri == '23.dkr.ecr.mars-south-3.amazonaws.com/sagemaker-mlfw:1.0rc-gpu-py3'
@@ -127,7 +139,8 @@ def test_tar_and_upload_dir_not_s3(sagemaker_session):
     script = os.path.basename(__file__)
     directory = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
     result = tar_and_upload_dir(sagemaker_session, bucket, s3_key_prefix, script, directory)
-    assert result == UploadedCode('s3://{}/{}/sourcedir.tar.gz'.format(bucket, s3_key_prefix), script)
+    assert result == UploadedCode('s3://{}/{}/sourcedir.tar.gz'.format(bucket, s3_key_prefix),
+                                  script)
 
 
 def test_framework_name_from_image_mxnet():
@@ -149,21 +162,24 @@ def test_legacy_name_from_framework_image():
 
 
 def test_legacy_name_from_wrong_framework():
-    framework, py_ver, tag = framework_name_from_image('123.dkr.ecr.us-west-2.amazonaws.com/sagemaker-myown-py2-gpu:1')
+    framework, py_ver, tag = framework_name_from_image(
+        '123.dkr.ecr.us-west-2.amazonaws.com/sagemaker-myown-py2-gpu:1')
     assert framework is None
     assert py_ver is None
     assert tag is None
 
 
 def test_legacy_name_from_wrong_python():
-    framework, py_ver, tag = framework_name_from_image('123.dkr.ecr.us-west-2.amazonaws.com/sagemaker-myown-py4-gpu:1')
+    framework, py_ver, tag = framework_name_from_image(
+        '123.dkr.ecr.us-west-2.amazonaws.com/sagemaker-myown-py4-gpu:1')
     assert framework is None
     assert py_ver is None
     assert tag is None
 
 
 def test_legacy_name_from_wrong_device():
-    framework, py_ver, tag = framework_name_from_image('123.dkr.ecr.us-west-2.amazonaws.com/sagemaker-myown-py4-gpu:1')
+    framework, py_ver, tag = framework_name_from_image(
+        '123.dkr.ecr.us-west-2.amazonaws.com/sagemaker-myown-py4-gpu:1')
     assert framework is None
     assert py_ver is None
     assert tag is None

tests/unit/test_tfs.py

@@ -20,8 +20,8 @@
 from mock import Mock
 
 from sagemaker.tensorflow import TensorFlow
-from sagemaker.tensorflow.serving import Model, Predictor
 from sagemaker.tensorflow.predictor import csv_serializer
+from sagemaker.tensorflow.serving import Model, Predictor
 
 JSON_CONTENT_TYPE = 'application/json'
 CSV_CONTENT_TYPE = 'text/csv'
@@ -94,7 +94,8 @@ def test_estimator_deploy(sagemaker_session):
 
     job_name = 'doing something'
     tf.fit(inputs='s3://mybucket/train', job_name=job_name)
-    predictor = tf.deploy(INSTANCE_COUNT, INSTANCE_TYPE, 'endpoint', endpoint_type='tensorflow-serving')
+    predictor = tf.deploy(INSTANCE_COUNT, INSTANCE_TYPE, 'endpoint',
+                          endpoint_type='tensorflow-serving')
     assert isinstance(predictor, Predictor)
 
 
@@ -190,8 +191,13 @@ def test_predictor_classify_bad_content_type():
         predictor.classify(CLASSIFY_INPUT)
 
 
-def assert_invoked(sagemaker_session, **args):
-    sagemaker_session.sagemaker_runtime_client.invoke_endpoint.assert_called_once_with(**args)
+def assert_invoked(sagemaker_session, **kwargs):
+    call = sagemaker_session.sagemaker_runtime_client.invoke_endpoint.call_args
+    cargs, ckwargs = call
+    assert not cargs
+    assert len(kwargs) == len(ckwargs)
+    for k in ckwargs:
+        assert kwargs[k] == ckwargs[k]
 
 
 def mock_response(expected_response, sagemaker_session, content_type=JSON_CONTENT_TYPE):