Deprecate enable_cloudwatch_metrics from Frameworks (aws#292)

There is a warning message for now, but it will be removed later.

Author: iquintero

CHANGELOG.rst

@@ -6,6 +6,7 @@ CHANGELOG
 ========
 
 * bug-fix: Estimators: Fix serialization of single records
+* bug-fix: deprecate enable_cloudwatch_metrics from Framework Estimators.
 
 1.9.0
 =====

src/sagemaker/estimator.py

@@ -15,6 +15,7 @@
 import json
 import logging
 import os
+import warnings
 from abc import ABCMeta
 from abc import abstractmethod
 from six import with_metaclass
@@ -550,8 +551,8 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
                 The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker.
                 For convenience, this accepts other types for keys and values, but ``str()`` will be called
                 to convert them before training.
-            enable_cloudwatch_metrics (bool): Whether training and hosting containers will
-               generate CloudWatch metrics under the AWS/SageMakerContainer namespace (default: False).
+            enable_cloudwatch_metrics (bool): [DEPRECATED] Now there are cloudwatch metrics emitted by all SageMaker
+                training jobs. This will be ignored for now and removed in a further release.
             container_log_level (int): Log level to use within the container (default: logging.INFO).
                 Valid values are defined in the Python logging module.
             code_location (str): Name of the S3 bucket where custom code is uploaded (default: None).
@@ -564,7 +565,10 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl
         super(Framework, self).__init__(**kwargs)
         self.source_dir = source_dir
         self.entry_point = entry_point
-        self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
+        if enable_cloudwatch_metrics:
+            warnings.warn('enable_cloudwatch_metrics is now deprecated and will be removed in the future.',
+                          DeprecationWarning)
+        self.enable_cloudwatch_metrics = False
         self.container_log_level = container_log_level
         self._hyperparameters = hyperparameters or {}
         self.code_location = code_location

src/sagemaker/mxnet/README.rst

@@ -543,9 +543,6 @@ The MXNetModel constructor takes the following arguments:
    directory with any other training source code dependencies including
    tne entry point file. Structure within this directory will be
    preserved when training on SageMaker.
--  ``enable_cloudwatch_metrics (boolean):`` Optional. If true, training
-   and hosting containers will generate Cloudwatch metrics under the
-   AWS/SageMakerContainer namespace.
 -  ``container_log_level (int):`` Log level to use within the container.
    Valid values are defined in the Python logging module.
 -  ``code_location (str):`` Optional. Name of the S3 bucket where your

tests/unit/test_chainer.py

@@ -66,15 +66,14 @@ def _get_full_gpu_image_uri(version):
 
 
 def _chainer_estimator(sagemaker_session, framework_version=defaults.CHAINER_VERSION, train_instance_type=None,
-                       enable_cloudwatch_metrics=False, base_job_name=None, use_mpi=None, num_processes=None,
+                       base_job_name=None, use_mpi=None, num_processes=None,
                        process_slots_per_host=None, additional_mpi_options=None, **kwargs):
     return Chainer(entry_point=SCRIPT_PATH,
                    framework_version=framework_version,
                    role=ROLE,
                    sagemaker_session=sagemaker_session,
                    train_instance_count=INSTANCE_COUNT,
                    train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
-                   enable_cloudwatch_metrics=enable_cloudwatch_metrics,
                    base_job_name=base_job_name,
                    use_mpi=use_mpi,
                    num_processes=num_processes,
@@ -152,7 +151,6 @@ def _create_train_job_with_additional_hyperparameters(version):
         },
         'hyperparameters': {
             'sagemaker_program': json.dumps('dummy_script.py'),
-            'sagemaker_enable_cloudwatch_metrics': 'false',
             'sagemaker_container_log_level': str(logging.INFO),
             'sagemaker_job_name': json.dumps(JOB_NAME),
             'sagemaker_submit_directory':
@@ -225,12 +223,10 @@ def test_attach_with_additional_hyperparameters(sagemaker_session, chainer_versi
 def test_create_model(sagemaker_session, chainer_version):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                       train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
                       framework_version=chainer_version, container_log_level=container_log_level,
-                      py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
-                      enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+                      py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)
 
     job_name = 'new_name'
     chainer.fit(inputs='s3://mybucket/train', job_name=job_name)
@@ -244,7 +240,6 @@ def test_create_model(sagemaker_session, chainer_version):
     assert model.name == job_name
     assert model.container_log_level == container_log_level
     assert model.source_dir == source_dir
-    assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics
 
 
 def test_create_model_with_optional_params(sagemaker_session):
@@ -269,13 +264,11 @@ def test_create_model_with_optional_params(sagemaker_session):
 def test_create_model_with_custom_image(sagemaker_session):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     custom_image = 'ubuntu:latest'
     chainer = Chainer(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                       train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
                       image_name=custom_image, container_log_level=container_log_level,
-                      py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir,
-                      enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+                      py_version=PYTHON_VERSION, base_job_name='job', source_dir=source_dir)
 
     chainer.fit(inputs='s3://mybucket/train', job_name='new_name')
     model = chainer.create_model()

tests/unit/test_mxnet.py

@@ -101,11 +101,10 @@ def _create_train_job(version):
 def test_create_model(sagemaker_session, mxnet_version):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
                framework_version=mxnet_version, container_log_level=container_log_level,
-               base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+               base_job_name='job', source_dir=source_dir)
 
     job_name = 'new_name'
     mx.fit(inputs='s3://mybucket/train', job_name=job_name)
@@ -119,7 +118,6 @@ def test_create_model(sagemaker_session, mxnet_version):
     assert model.name == job_name
     assert model.container_log_level == container_log_level
     assert model.source_dir == source_dir
-    assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics
 
 
 def test_create_model_with_optional_params(sagemaker_session):
@@ -144,12 +142,11 @@ def test_create_model_with_optional_params(sagemaker_session):
 def test_create_model_with_custom_image(sagemaker_session):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     custom_image = 'mxnet:2.0'
     mx = MXNet(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
                image_name=custom_image, container_log_level=container_log_level,
-               base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+               base_job_name='job', source_dir=source_dir)
 
     job_name = 'new_name'
     mx.fit(inputs='s3://mybucket/train', job_name='new_name')
@@ -162,7 +159,6 @@ def test_create_model_with_custom_image(sagemaker_session):
     assert model.name == job_name
     assert model.container_log_level == container_log_level
     assert model.source_dir == source_dir
-    assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics
 
 
 @patch('time.strftime', return_value=TIMESTAMP)

tests/unit/test_pytorch.py

@@ -64,15 +64,14 @@ def _get_full_gpu_image_uri(version, py_version=PYTHON_VERSION):
 
 
 def _pytorch_estimator(sagemaker_session, framework_version=defaults.PYTORCH_VERSION, train_instance_type=None,
-                       enable_cloudwatch_metrics=False, base_job_name=None, **kwargs):
+                       base_job_name=None, **kwargs):
     return PyTorch(entry_point=SCRIPT_PATH,
                    framework_version=framework_version,
                    py_version=PYTHON_VERSION,
                    role=ROLE,
                    sagemaker_session=sagemaker_session,
                    train_instance_count=INSTANCE_COUNT,
                    train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
-                   enable_cloudwatch_metrics=enable_cloudwatch_metrics,
                    base_job_name=base_job_name,
                    **kwargs)
 
@@ -119,11 +118,10 @@ def _create_train_job(version):
 def test_create_model(sagemaker_session, pytorch_version):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                       train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
                       framework_version=pytorch_version, container_log_level=container_log_level,
-                      base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+                      base_job_name='job', source_dir=source_dir)
 
     job_name = 'new_name'
     pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
@@ -137,7 +135,6 @@ def test_create_model(sagemaker_session, pytorch_version):
     assert model.name == job_name
     assert model.container_log_level == container_log_level
     assert model.source_dir == source_dir
-    assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics
 
 
 def test_create_model_with_optional_params(sagemaker_session):
@@ -162,12 +159,11 @@ def test_create_model_with_optional_params(sagemaker_session):
 def test_create_model_with_custom_image(sagemaker_session):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     image = 'pytorch:9000'
     pytorch = PyTorch(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                       train_instance_count=INSTANCE_COUNT, train_instance_type=INSTANCE_TYPE,
                       container_log_level=container_log_level, image_name=image,
-                      base_job_name='job', source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+                      base_job_name='job', source_dir=source_dir)
 
     job_name = 'new_name'
     pytorch.fit(inputs='s3://mybucket/train', job_name='new_name')
@@ -180,7 +176,6 @@ def test_create_model_with_custom_image(sagemaker_session):
     assert model.name == job_name
     assert model.container_log_level == container_log_level
     assert model.source_dir == source_dir
-    assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics
 
 
 @patch('time.strftime', return_value=TIMESTAMP)

tests/unit/test_tf_estimator.py

@@ -107,7 +107,7 @@ def _create_train_job(tf_version):
 
 
 def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_instance_type=None,
-              checkpoint_path=None, enable_cloudwatch_metrics=False, base_job_name=None,
+              checkpoint_path=None, base_job_name=None,
               training_steps=None, evaluation_steps=None, **kwargs):
     return TensorFlow(entry_point=SCRIPT_PATH,
                       training_steps=training_steps,
@@ -118,7 +118,6 @@ def _build_tf(sagemaker_session, framework_version=defaults.TF_VERSION, train_in
                       train_instance_count=INSTANCE_COUNT,
                       train_instance_type=train_instance_type if train_instance_type else INSTANCE_TYPE,
                       checkpoint_path=checkpoint_path,
-                      enable_cloudwatch_metrics=enable_cloudwatch_metrics,
                       base_job_name=base_job_name,
                       **kwargs)
 
@@ -183,12 +182,11 @@ def test_tf_nonexistent_requirements_path(sagemaker_session):
 def test_create_model(sagemaker_session, tf_version):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                     training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
                     train_instance_type=INSTANCE_TYPE, framework_version=tf_version,
                     container_log_level=container_log_level, base_job_name='job',
-                    source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+                    source_dir=source_dir)
 
     job_name = 'doing something'
     tf.fit(inputs='s3://mybucket/train', job_name=job_name)
@@ -202,7 +200,6 @@ def test_create_model(sagemaker_session, tf_version):
     assert model.name == job_name
     assert model.container_log_level == container_log_level
     assert model.source_dir == source_dir
-    assert model.enable_cloudwatch_metrics == enable_cloudwatch_metrics
 
 
 def test_create_model_with_optional_params(sagemaker_session):
@@ -228,13 +225,12 @@ def test_create_model_with_optional_params(sagemaker_session):
 def test_create_model_with_custom_image(sagemaker_session):
     container_log_level = '"logging.INFO"'
     source_dir = 's3://mybucket/source'
-    enable_cloudwatch_metrics = 'true'
     custom_image = 'tensorflow:1.0'
     tf = TensorFlow(entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session,
                     training_steps=1000, evaluation_steps=10, train_instance_count=INSTANCE_COUNT,
                     train_instance_type=INSTANCE_TYPE, image_name=custom_image,
                     container_log_level=container_log_level, base_job_name='job',
-                    source_dir=source_dir, enable_cloudwatch_metrics=enable_cloudwatch_metrics)
+                    source_dir=source_dir)
 
     job_name = 'doing something'
     tf.fit(inputs='s3://mybucket/train', job_name=job_name)