Merge branch 'master' into stopiteration

ChoiByungWook · web-flow · commit f8f3dc30fd0d · 2019-01-10T18:12:34.000-08:00
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -2,12 +2,17 @@
 CHANGELOG
 =========
 
-1.17.1.dev
+1.17.2.dev
 ==========
 
-* enhancement: Workflow: Specify tasks from which training/tuning operator to transform/deploy in related operators
 * bug-fix: Handle StopIteration in CloudWatch Logs retrieval
 
+1.17.1
+======
+
+* enhancement: Workflow: Specify tasks from which training/tuning operator to transform/deploy in related operators
+* feature: Supporting inter-container traffic encryption flag
+
 1.17.0
 ======
 
diff --git a/README.rst b/README.rst
@@ -748,6 +748,26 @@ To train a model using your own VPC, set the optional parameters ``subnets`` and
     # SageMaker Training Job will set VpcConfig and container instances will run in your VPC
     mxnet_vpc_estimator.fit('s3://my_bucket/my_training_data/')
 
+To train a model with the inter-container traffic encrypted, set the optional parameters ``subnets`` and ``security_group_ids`` and
+the flag ``encrypt_inter_container_traffic`` as ``True`` on an Estimator (Note: This flag can be used only if you specify that the training
+job runs in a VPC):
+
+.. code:: python
+
+    from sagemaker.mxnet import MXNet
+
+    # Configure an MXNet Estimator with subnets and security groups from your VPC
+    mxnet_vpc_estimator = MXNet('train.py',
+                                train_instance_type='ml.p2.xlarge',
+                                train_instance_count=1,
+                                framework_version='1.2.1',
+                                subnets=['subnet-1', 'subnet-2'],
+                                security_group_ids=['sg-1'],
+                                encrypt_inter_container_traffic=True)
+
+    # The SageMaker training job sets the VpcConfig, and training container instances run in your VPC with traffic between the containers encrypted
+    mxnet_vpc_estimator.fit('s3://my_bucket/my_training_data/')
+
 When you create a ``Predictor`` from the ``Estimator`` using ``deploy()``, the same VPC configurations will be set on the SageMaker Model:
 
 .. code:: python
diff --git a/doc/conf.py b/doc/conf.py
@@ -32,7 +32,7 @@ def __getattr__(cls, name):
                 'numpy', 'scipy', 'scipy.sparse']
 sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 
-version = '1.17.0'
+version = '1.17.1'
 project = u'sagemaker'
 
 # Add any Sphinx extension module names here, as strings. They can be extensions
diff --git a/src/sagemaker/__init__.py b/src/sagemaker/__init__.py
@@ -39,4 +39,4 @@
 from sagemaker.session import s3_input  # noqa: F401
 from sagemaker.session import get_execution_role  # noqa: F401
 
-__version__ = '1.17.0'
+__version__ = '1.17.1'
diff --git a/src/sagemaker/algorithm.py b/src/sagemaker/algorithm.py
@@ -50,6 +50,7 @@ def __init__(
         model_uri=None,
         model_channel_name='model',
         metric_definitions=None,
+        encrypt_inter_container_traffic=False
     ):
         """Initialize an ``AlgorithmEstimator`` instance.
 
@@ -75,7 +76,7 @@ def __init__(
                 * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe.
 
                 This argument can be overriden on a per-channel basis using ``sagemaker.session.s3_input.input_mode``.
-            output_path (str): S3 location for saving the trainig result (model artifacts and output files).
+            output_path (str): S3 location for saving the training result (model artifacts and output files).
                 If not specified, results are stored to a default bucket. If the bucket with the specific name
                 does not exist, the estimator creates the bucket during the
                 :meth:`~sagemaker.estimator.EstimatorBase.fit` method execution.
@@ -100,6 +101,8 @@ def __init__(
             metric_definitions (list[dict]): A list of dictionaries that defines the metric(s) used to evaluate the
                 training jobs. Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for
                 the regular expression used to extract the metric from the logs.
+            encrypt_inter_container_traffic (bool): Specifies whether traffic between training containers is encrypted
+                for the training job (default: ``False``).
         """
         self.algorithm_arn = algorithm_arn
         super(AlgorithmEstimator, self).__init__(
@@ -120,6 +123,7 @@ def __init__(
             model_uri=model_uri,
             model_channel_name=model_channel_name,
             metric_definitions=metric_definitions,
+            encrypt_inter_container_traffic=encrypt_inter_container_traffic
         )
 
         self.algorithm_spec = self.sagemaker_session.sagemaker_client.describe_algorithm(
diff --git a/src/sagemaker/estimator.py b/src/sagemaker/estimator.py
@@ -52,7 +52,7 @@ def __init__(self, role, train_instance_count, train_instance_type,
                  train_volume_size=30, train_volume_kms_key=None, train_max_run=24 * 60 * 60, input_mode='File',
                  output_path=None, output_kms_key=None, base_job_name=None, sagemaker_session=None, tags=None,
                  subnets=None, security_group_ids=None, model_uri=None, model_channel_name='model',
-                 metric_definitions=None):
+                 metric_definitions=None, encrypt_inter_container_traffic=False):
         """Initialize an ``EstimatorBase`` instance.
 
         Args:
@@ -103,6 +103,8 @@ def __init__(self, role, train_instance_count, train_instance_type,
                 training jobs. Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for
                 the regular expression used to extract the metric from the logs. This should be defined only
                 for jobs that don't use an Amazon algorithm.
+            encrypt_inter_container_traffic (bool): Specifies whether traffic between training containers is encrypted
+                for the training job (default: ``False``).
         """
         self.role = role
         self.train_instance_count = train_instance_count
@@ -138,6 +140,8 @@ def __init__(self, role, train_instance_count, train_instance_type,
         self.subnets = subnets
         self.security_group_ids = security_group_ids
 
+        self.encrypt_inter_container_traffic = encrypt_inter_container_traffic
+
     @abstractmethod
     def train_image(self):
         """Return the Docker image to use for training.
@@ -429,6 +433,10 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
         if 'MetricDefinitons' in job_details['AlgorithmSpecification']:
             init_params['metric_definitions'] = job_details['AlgorithmSpecification']['MetricsDefinition']
 
+        if 'EnableInterContainerTrafficEncryption' in job_details:
+            init_params['encrypt_inter_container_traffic'] = \
+                job_details['EnableInterContainerTrafficEncryption']
+
         subnets, security_group_ids = vpc_utils.from_dict(job_details.get(vpc_utils.VPC_CONFIG_KEY))
         if subnets:
             init_params['subnets'] = subnets
@@ -555,6 +563,9 @@ def start_new(cls, estimator, inputs):
         if estimator.enable_network_isolation():
             train_args['enable_network_isolation'] = True
 
+        if estimator.encrypt_inter_container_traffic:
+            train_args['encrypt_inter_container_traffic'] = True
+
         if isinstance(estimator, sagemaker.algorithm.AlgorithmEstimator):
             train_args['algorithm_arn'] = estimator.algorithm_arn
         else:
@@ -585,7 +596,8 @@ def __init__(self, image_name, role, train_instance_count, train_instance_type,
                  train_volume_size=30, train_volume_kms_key=None, train_max_run=24 * 60 * 60,
                  input_mode='File', output_path=None, output_kms_key=None, base_job_name=None,
                  sagemaker_session=None, hyperparameters=None, tags=None, subnets=None, security_group_ids=None,
-                 model_uri=None, model_channel_name='model', metric_definitions=None):
+                 model_uri=None, model_channel_name='model', metric_definitions=None,
+                 encrypt_inter_container_traffic=False):
         """Initialize an ``Estimator`` instance.
 
         Args:
@@ -640,14 +652,17 @@ def __init__(self, image_name, role, train_instance_count, train_instance_type,
                 training jobs. Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for
                 the regular expression used to extract the metric from the logs. This should be defined only
                 for jobs that don't use an Amazon algorithm.
+            encrypt_inter_container_traffic (bool): Specifies whether traffic between training containers is encrypted
+                for the training job (default: ``False``).
         """
         self.image_name = image_name
         self.hyperparam_dict = hyperparameters.copy() if hyperparameters else {}
         super(Estimator, self).__init__(role, train_instance_count, train_instance_type,
                                         train_volume_size, train_volume_kms_key, train_max_run, input_mode,
                                         output_path, output_kms_key, base_job_name, sagemaker_session,
                                         tags, subnets, security_group_ids, model_uri=model_uri,
-                                        model_channel_name=model_channel_name, metric_definitions=metric_definitions)
+                                        model_channel_name=model_channel_name, metric_definitions=metric_definitions,
+                                        encrypt_inter_container_traffic=encrypt_inter_container_traffic)
 
     def train_image(self):
         """
@@ -743,7 +758,7 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
             entry_point (str): Path (absolute or relative) to the local Python source file which should be executed
                 as the entry point to training. This should be compatible with either Python 2.7 or Python 3.5.
             source_dir (str): Path (absolute or relative) to a directory with any other training
-                source code dependencies aside from tne entry point file (default: None). Structure within this
+                source code dependencies aside from the entry point file (default: None). Structure within this
                 directory are preserved when training on Amazon SageMaker.
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container (default: []).
diff --git a/src/sagemaker/session.py b/src/sagemaker/session.py
@@ -215,7 +215,8 @@ def default_bucket(self):
 
     def train(self, input_mode, input_config, role, job_name, output_config,  # noqa: C901
               resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions,
-              enable_network_isolation=False, image=None, algorithm_arn=None):
+              enable_network_isolation=False, image=None, algorithm_arn=None,
+              encrypt_inter_container_traffic=False):
         """Create an Amazon SageMaker training job.
 
         Args:
@@ -261,6 +262,8 @@ def train(self, input_mode, input_config, role, job_name, output_config,  # noqa
                 network isolation or not.
             image (str): Docker image containing training code.
             algorithm_arn (str): Algorithm Arn from Marketplace.
+            encrypt_inter_container_traffic (bool): Specifies whether traffic between training containers is
+                encrypted for the training job (default: ``False``).
 
         Returns:
             str: ARN of the training job, if it is created.
@@ -308,6 +311,10 @@ def train(self, input_mode, input_config, role, job_name, output_config,  # noqa
         if enable_network_isolation:
             train_request['EnableNetworkIsolation'] = enable_network_isolation
 
+        if encrypt_inter_container_traffic:
+            train_request['EnableInterContainerTrafficEncryption'] = \
+                encrypt_inter_container_traffic
+
         LOGGER.info('Creating training-job with name: {}'.format(job_name))
         LOGGER.debug('train request: {}'.format(json.dumps(train_request, indent=4)))
         self.sagemaker_client.create_training_job(**train_request)
@@ -351,7 +358,7 @@ def tune(self, job_name, strategy, objective_type, objective_metric_name,
              static_hyperparameters, input_mode, metric_definitions,
              role, input_config, output_config, resource_config, stop_condition, tags,
              warm_start_config, enable_network_isolation=False, image=None, algorithm_arn=None,
-             early_stopping_type='Off'):
+             early_stopping_type='Off', encrypt_inter_container_traffic=False):
         """Create an Amazon SageMaker hyperparameter tuning job
 
         Args:
@@ -400,6 +407,9 @@ def tune(self, job_name, strategy, objective_type, objective_metric_name,
             early_stopping_type (str): Specifies whether early stopping is enabled for the job.
                 Can be either 'Auto' or 'Off'. If set to 'Off', early stopping will not be attempted.
                 If set to 'Auto', early stopping of some training jobs may happen, but is not guaranteed to.
+            encrypt_inter_container_traffic (bool): Specifies whether traffic between training containers
+                is encrypted for the training jobs started for this hyperparameter tuning job. Set to ``False``
+                by default.
         """
         tune_request = {
             'HyperParameterTuningJobName': job_name,
@@ -450,6 +460,9 @@ def tune(self, job_name, strategy, objective_type, objective_metric_name,
         if enable_network_isolation:
             tune_request['TrainingJobDefinition']['EnableNetworkIsolation'] = True
 
+        if encrypt_inter_container_traffic:
+            tune_request['TrainingJobDefinition']['EnableInterContainerTrafficEncryption'] = True
+
         LOGGER.info('Creating hyperparameter tuning job with name: {}'.format(job_name))
         LOGGER.debug('tune request: {}'.format(json.dumps(tune_request, indent=4)))
         self.sagemaker_client.create_hyper_parameter_tuning_job(**tune_request)
diff --git a/src/sagemaker/tuner.py b/src/sagemaker/tuner.py
@@ -641,6 +641,8 @@ def start_new(cls, tuner, inputs):
             tuner_args['image'] = tuner.estimator.train_image()
 
         tuner_args['enable_network_isolation'] = tuner.estimator.enable_network_isolation()
+        tuner_args['encrypt_inter_container_traffic'] = \
+            tuner.estimator.encrypt_inter_container_traffic
 
         tuner.estimator.sagemaker_session.tune(**tuner_args)
 
diff --git a/tests/integ/test_tf.py b/tests/integ/test_tf.py
@@ -22,7 +22,7 @@
 from sagemaker.utils import sagemaker_timestamp
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, PYTHON_VERSION
 from tests.integ.timeout import timeout_and_delete_endpoint_by_name, timeout
-from tests.integ.vpc_test_utils import get_or_create_vpc_resources
+from tests.integ.vpc_test_utils import get_or_create_vpc_resources, setup_security_group_for_encryption
 
 DATA_PATH = os.path.join(DATA_DIR, 'iris', 'data')
 
@@ -149,6 +149,8 @@ def test_tf_vpc_multi(sagemaker_session, tf_full_version):
     subnet_ids, security_group_id = get_or_create_vpc_resources(ec2_client,
                                                                 sagemaker_session.boto_session.region_name)
 
+    setup_security_group_for_encryption(ec2_client, security_group_id)
+
     estimator = TensorFlow(entry_point=script_path,
                            role='SageMakerRole',
                            framework_version=tf_full_version,
@@ -160,7 +162,8 @@ def test_tf_vpc_multi(sagemaker_session, tf_full_version):
                            sagemaker_session=sagemaker_session,
                            base_job_name='test-vpc-tf',
                            subnets=subnet_ids,
-                           security_group_ids=[security_group_id])
+                           security_group_ids=[security_group_id],
+                           encrypt_inter_container_traffic=True)
 
     with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
         estimator.fit(train_input)
@@ -170,6 +173,7 @@ def test_tf_vpc_multi(sagemaker_session, tf_full_version):
         TrainingJobName=estimator.latest_training_job.name)
     assert set(subnet_ids) == set(job_desc['VpcConfig']['Subnets'])
     assert [security_group_id] == job_desc['VpcConfig']['SecurityGroupIds']
+    assert job_desc['EnableInterContainerTrafficEncryption'] is True
 
     endpoint_name = estimator.latest_training_job.name
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
diff --git a/tests/integ/vpc_test_utils.py b/tests/integ/vpc_test_utils.py
@@ -89,3 +89,17 @@ def get_or_create_vpc_resources(ec2_client, region, name=VPC_NAME):
     else:
         print('creating new vpc: {}'.format(name))
         return _create_vpc_with_name(ec2_client, region, name)
+
+
+def setup_security_group_for_encryption(ec2_client, security_group_id):
+    sg_desc = ec2_client.describe_security_groups(GroupIds=[security_group_id])
+    ingress_perms = sg_desc['SecurityGroups'][0]['IpPermissions']
+    if len(ingress_perms) == 1:
+        ec2_client.\
+            authorize_security_group_ingress(GroupId=security_group_id,
+                                             IpPermissions=[{'IpProtocol': '50',
+                                                             'UserIdGroupPairs': [{'GroupId': security_group_id}]},
+                                                            {'IpProtocol': 'udp',
+                                                             'FromPort': 500,
+                                                             'ToPort': 500,
+                                                             'UserIdGroupPairs': [{'GroupId': security_group_id}]}])
diff --git a/tests/unit/test_algorithm.py b/tests/unit/test_algorithm.py
@@ -894,3 +894,22 @@ def test_algorithm_enable_network_isolation_with_product_id(sagemaker_session):
 
     network_isolation = estimator.enable_network_isolation()
     assert network_isolation is True
+
+
+def test_algorithm_encrypt_inter_container_traffic(sagemaker_session):
+    response = copy.deepcopy(DESCRIBE_ALGORITHM_RESPONSE)
+    response['encrypt_inter_container_traffic'] = True
+    sagemaker_session.sagemaker_client.describe_algorithm = Mock(
+        return_value=response)
+
+    estimator = AlgorithmEstimator(
+        algorithm_arn='arn:aws:sagemaker:us-east-2:1234:algorithm/scikit-decision-trees',
+        role='SageMakerRole',
+        train_instance_type='ml.m4.xlarge',
+        train_instance_count=1,
+        sagemaker_session=sagemaker_session,
+        encrypt_inter_container_traffic=True
+    )
+
+    encrypt_inter_container_traffic = estimator.encrypt_inter_container_traffic
+    assert encrypt_inter_container_traffic is True
diff --git a/tests/unit/test_estimator.py b/tests/unit/test_estimator.py
diff --git a/tests/unit/test_session.py b/tests/unit/test_session.py
diff --git a/tests/unit/test_tuner.py b/tests/unit/test_tuner.py
