change: deprecate unused parameters

Author: Chuyang Deng

doc/algorithms/randomcutforest.rst

@@ -8,7 +8,7 @@ The Amazon SageMaker Random Cut Forest algorithm.
     :undoc-members:
     :show-inheritance:
     :inherited-members:
-    :exclude-members: image_uri, num_trees, num_samples_per_tree, eval_metrics, feature_dim, MINI_BATCH_SIZE
+    :exclude-members: image_uri, num_trees, num_samples_per_tree, feature_dim, MINI_BATCH_SIZE
 
 
 .. autoclass:: sagemaker.RandomCutForestModel

src/sagemaker/amazon/kmeans.py

@@ -43,11 +43,6 @@ class KMeans(AmazonAlgorithmEstimatorBase):
     )
     epochs = hp("epochs", gt(0), "An integer greater-than 0", int)
     center_factor = hp("extra_center_factor", gt(0), "An integer greater-than 0", int)
-    eval_metrics = hp(
-        name="eval_metrics",
-        validation_message='A comma separated list of "msd" or "ssd"',
-        data_type=list,
-    )
 
     def __init__(
         self,
@@ -63,7 +58,6 @@ def __init__(
         half_life_time_size=None,
         epochs=None,
         center_factor=None,
-        eval_metrics=None,
         **kwargs
     ):
         """A k-means clustering
@@ -130,11 +124,6 @@ def __init__(
             center_factor (int): The algorithm will create
                 ``num_clusters * extra_center_factor`` as it runs and reduce the
                 number of centers to ``k`` when finalizing
-            eval_metrics (list): JSON list of metrics types to be used for
-                reporting the score for the model. Allowed values are "msd"
-                Means Square Error, "ssd": Sum of square distance. If test data
-                is provided, the score shall be reported in terms of all
-                requested metrics.
             **kwargs: base class keyword argument values.
 
         .. tip::
@@ -153,7 +142,6 @@ def __init__(
         self.half_life_time_size = half_life_time_size
         self.epochs = epochs
         self.center_factor = center_factor
-        self.eval_metrics = eval_metrics
 
     def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.kmeans.KMeansModel` referencing

src/sagemaker/amazon/randomcutforest.py

@@ -31,12 +31,6 @@ class RandomCutForest(AmazonAlgorithmEstimatorBase):
     repo_version = 1
     MINI_BATCH_SIZE = 1000
 
-    eval_metrics = hp(
-        name="eval_metrics",
-        validation_message='A comma separated list of "accuracy" or "precision_recall_fscore"',
-        data_type=list,
-    )
-
     num_trees = hp("num_trees", (ge(50), le(1000)), "An integer in [50, 1000]", int)
     num_samples_per_tree = hp(
         "num_samples_per_tree", (ge(1), le(2048)), "An integer in [1, 2048]", int
@@ -50,7 +44,6 @@ def __init__(
         instance_type,
         num_samples_per_tree=None,
         num_trees=None,
-        eval_metrics=None,
         **kwargs
     ):
         """RandomCutForest is :class:`Estimator` used for anomaly detection.
@@ -99,11 +92,6 @@ def __init__(
                 build each tree in the forest. The total number of samples drawn
                 from the train dataset is num_trees * num_samples_per_tree.
             num_trees (int): Optional. The number of trees used in the forest.
-            eval_metrics (list): Optional. JSON list of metrics types to be used
-                for reporting the score for the model. Allowed values are
-                "accuracy", "precision_recall_fscore": positive and negative
-                precision, recall, and f1 scores. If test data is provided, the
-                score shall be reported in terms of all requested metrics.
             **kwargs: base class keyword argument values.
 
         .. tip::
@@ -116,7 +104,6 @@ def __init__(
         super(RandomCutForest, self).__init__(role, instance_count, instance_type, **kwargs)
         self.num_samples_per_tree = num_samples_per_tree
         self.num_trees = num_trees
-        self.eval_metrics = eval_metrics
 
     def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         """Return a :class:`~sagemaker.amazon.RandomCutForestModel` referencing

src/sagemaker/chainer/estimator.py

@@ -216,7 +216,6 @@ def create_model(
             role or self.role,
             entry_point or self._model_entry_point(),
             source_dir=(source_dir or self._model_source_dir()),
-            enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             py_version=self.py_version,

src/sagemaker/estimator.py

@@ -17,7 +17,6 @@
 import logging
 import os
 import uuid
-import warnings
 from abc import ABCMeta
 from abc import abstractmethod
 
@@ -46,7 +45,6 @@
 from sagemaker.model import (
     SCRIPT_PARAM_NAME,
     DIR_PARAM_NAME,
-    CLOUDWATCH_METRICS_PARAM_NAME,
     CONTAINER_LOG_LEVEL_PARAM_NAME,
     JOB_NAME_PARAM_NAME,
     SAGEMAKER_REGION_PARAM_NAME,
@@ -1433,7 +1431,6 @@ def __init__(
         entry_point,
         source_dir=None,
         hyperparameters=None,
-        enable_cloudwatch_metrics=False,
         container_log_level=logging.INFO,
         code_location=None,
         image_uri=None,
@@ -1491,9 +1488,6 @@ def __init__(
                 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): [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.
@@ -1624,12 +1618,6 @@ def __init__(
         self.dependencies = dependencies or []
         self.uploaded_code = None
 
-        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.code_location = code_location
         self.image_uri = image_uri
@@ -1687,7 +1675,6 @@ def _prepare_for_training(self, job_name=None):
         # Modify hyperparameters in-place to point to the right code directory and script URIs
         self._hyperparameters[DIR_PARAM_NAME] = code_dir
         self._hyperparameters[SCRIPT_PARAM_NAME] = script
-        self._hyperparameters[CLOUDWATCH_METRICS_PARAM_NAME] = self.enable_cloudwatch_metrics
         self._hyperparameters[CONTAINER_LOG_LEVEL_PARAM_NAME] = self.container_log_level
         self._hyperparameters[JOB_NAME_PARAM_NAME] = self._current_job_name
         self._hyperparameters[SAGEMAKER_REGION_PARAM_NAME] = self.sagemaker_session.boto_region_name
@@ -1798,9 +1785,6 @@ class constructor
             init_params["hyperparameters"].get(SCRIPT_PARAM_NAME)
         )
         init_params["source_dir"] = json.loads(init_params["hyperparameters"].get(DIR_PARAM_NAME))
-        init_params["enable_cloudwatch_metrics"] = json.loads(
-            init_params["hyperparameters"].get(CLOUDWATCH_METRICS_PARAM_NAME)
-        )
         init_params["container_log_level"] = json.loads(
             init_params["hyperparameters"].get(CONTAINER_LOG_LEVEL_PARAM_NAME)
         )

src/sagemaker/model.py

@@ -599,7 +599,6 @@ def delete_model(self):
 
 SCRIPT_PARAM_NAME = "sagemaker_program"
 DIR_PARAM_NAME = "sagemaker_submit_directory"
-CLOUDWATCH_METRICS_PARAM_NAME = "sagemaker_enable_cloudwatch_metrics"
 CONTAINER_LOG_LEVEL_PARAM_NAME = "sagemaker_container_log_level"
 JOB_NAME_PARAM_NAME = "sagemaker_job_name"
 MODEL_SERVER_WORKERS_PARAM_NAME = "sagemaker_model_server_workers"
@@ -624,7 +623,6 @@ def __init__(
         predictor_cls=None,
         env=None,
         name=None,
-        enable_cloudwatch_metrics=False,
         container_log_level=logging.INFO,
         code_location=None,
         sagemaker_session=None,
@@ -682,9 +680,6 @@ def __init__(
                 when hosted in SageMaker (default: None).
             name (str): The model name. If None, a default model name will be
                 selected on each ``deploy``.
-            enable_cloudwatch_metrics (bool): Whether training and hosting
-                containers will generate CloudWatch metrics under the
-                AWS/SageMakerContainer namespace (default: False).
             container_log_level (int): Log level to use within the container
                 (default: logging.INFO). Valid values are defined in the Python
                 logging module.
@@ -792,7 +787,6 @@ def __init__(
         self.source_dir = source_dir
         self.dependencies = dependencies or []
         self.git_config = git_config
-        self.enable_cloudwatch_metrics = enable_cloudwatch_metrics
         self.container_log_level = container_log_level
         if code_location:
             self.bucket, self.key_prefix = fw_utils.parse_s3_url(code_location)
@@ -890,7 +884,6 @@ def _framework_env_vars(self):
         return {
             SCRIPT_PARAM_NAME.upper(): script_name,
             DIR_PARAM_NAME.upper(): dir_name,
-            CLOUDWATCH_METRICS_PARAM_NAME.upper(): str(self.enable_cloudwatch_metrics).lower(),
             CONTAINER_LOG_LEVEL_PARAM_NAME.upper(): str(self.container_log_level),
             SAGEMAKER_REGION_PARAM_NAME.upper(): self.sagemaker_session.boto_region_name,
         }

src/sagemaker/mxnet/estimator.py

@@ -225,7 +225,6 @@ def create_model(
             framework_version=self.framework_version,
             py_version=self.py_version,
             source_dir=(source_dir or self._model_source_dir()),
-            enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             model_server_workers=model_server_workers,

src/sagemaker/pytorch/estimator.py

@@ -179,7 +179,6 @@ def create_model(
             framework_version=self.framework_version,
             py_version=self.py_version,
             source_dir=(source_dir or self._model_source_dir()),
-            enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             model_server_workers=model_server_workers,

src/sagemaker/rl/estimator.py

@@ -244,7 +244,6 @@ def create_model(
             source_dir=source_dir,
             code_location=self.code_location,
             dependencies=dependencies,
-            enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
         )
         extended_args.update(base_args)
 

src/sagemaker/sklearn/estimator.py

@@ -201,7 +201,6 @@ def create_model(
             role,
             entry_point or self._model_entry_point(),
             source_dir=(source_dir or self._model_source_dir()),
-            enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             py_version=self.py_version,

src/sagemaker/workflow/airflow.py

@@ -59,9 +59,6 @@ def prepare_framework(estimator, s3_operations):
         ]
     estimator._hyperparameters[sagemaker.model.DIR_PARAM_NAME] = code_dir
     estimator._hyperparameters[sagemaker.model.SCRIPT_PARAM_NAME] = script
-    estimator._hyperparameters[
-        sagemaker.model.CLOUDWATCH_METRICS_PARAM_NAME
-    ] = estimator.enable_cloudwatch_metrics
     estimator._hyperparameters[
         sagemaker.model.CONTAINER_LOG_LEVEL_PARAM_NAME
     ] = estimator.container_log_level

src/sagemaker/xgboost/estimator.py

@@ -163,7 +163,6 @@ def create_model(
             entry_point or self._model_entry_point(),
             framework_version=self.framework_version,
             source_dir=(source_dir or self._model_source_dir()),
-            enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
             py_version=self.py_version,

tests/component/test_mxnet_estimator.py

@@ -75,7 +75,6 @@ def test_deploy(sagemaker_session, tf_version):
         ROLE,
         {
             "Environment": {
-                "SAGEMAKER_ENABLE_CLOUDWATCH_METRICS": "false",
                 "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
                 "SAGEMAKER_SUBMIT_DIRECTORY": SOURCE_DIR,
                 "SAGEMAKER_REGION": REGION,

tests/component/test_tf_estimator.py

@@ -74,7 +74,6 @@ def test_deploy(sagemaker_session, tf_version):
         ROLE,
         {
             "Environment": {
-                "SAGEMAKER_ENABLE_CLOUDWATCH_METRICS": "false",
                 "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
                 "SAGEMAKER_SUBMIT_DIRECTORY": SOURCE_DIR,
                 "SAGEMAKER_REQUIREMENTS": "",

tests/integ/test_airflow_config.py

@@ -111,7 +111,6 @@ def test_kmeans_airflow_config_uploads_data_source_to_s3(sagemaker_session, cpu_
         kmeans.half_life_time_size = 1
         kmeans.epochs = 1
         kmeans.center_factor = 1
-        kmeans.eval_metrics = ["ssd", "msd"]
 
         records = kmeans.record_set(datasets.one_p_mnist()[0][:100])
 
@@ -386,7 +385,6 @@ def test_rcf_airflow_config_uploads_data_source_to_s3(sagemaker_session, cpu_ins
             instance_type=cpu_instance_type,
             num_trees=50,
             num_samples_per_tree=20,
-            eval_metrics=["accuracy", "precision_recall_fscore"],
             sagemaker_session=sagemaker_session,
         )
 

tests/integ/test_data_capture_config.py

@@ -32,7 +32,6 @@
 ENVIRONMENT = {"env_key_1": "env_value_1"}
 TAGS = [{"Key": "tag_key_1", "Value": "tag_value_1"}]
 NETWORK_CONFIG = NetworkConfig(enable_network_isolation=True)
-ENABLE_CLOUDWATCH_METRICS = True
 
 CUSTOM_SAMPLING_PERCENTAGE = 10
 CUSTOM_CAPTURE_OPTIONS = ["REQUEST"]

tests/integ/test_kmeans.py

@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
-import json
 import time
 
 import pytest
@@ -47,7 +46,6 @@ def test_kmeans(sagemaker_session, cpu_instance_type, training_set):
         kmeans.half_life_time_size = 1
         kmeans.epochs = 1
         kmeans.center_factor = 1
-        kmeans.eval_metrics = ["ssd", "msd"]
 
         assert kmeans.hyperparameters() == dict(
             init_method=kmeans.init_method,
@@ -59,7 +57,6 @@ def test_kmeans(sagemaker_session, cpu_instance_type, training_set):
             epochs=str(kmeans.epochs),
             extra_center_factor=str(kmeans.center_factor),
             k=str(kmeans.k),
-            eval_metrics=json.dumps(kmeans.eval_metrics),
             force_dense="True",
         )
 

tests/integ/test_multidatamodel.py

@@ -439,7 +439,6 @@ def __rcf_training_job(
             instance_type=cpu_instance_type,
             num_trees=num_trees,
             num_samples_per_tree=num_samples_per_tree,
-            eval_metrics=["accuracy", "precision_recall_fscore"],
             sagemaker_session=sagemaker_session,
         )
 

tests/unit/sagemaker/model/test_framework_model.py

@@ -106,7 +106,6 @@ def test_prepare_container_def(time, sagemaker_session):
             "SAGEMAKER_SUBMIT_DIRECTORY": "s3://mybucket/mi-2017-10-10-14-14-15/sourcedir.tar.gz",
             "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
             "SAGEMAKER_REGION": REGION,
-            "SAGEMAKER_ENABLE_CLOUDWATCH_METRICS": "false",
         },
         "Image": MODEL_IMAGE,
         "ModelDataUrl": MODEL_DATA,
@@ -125,7 +124,6 @@ def test_prepare_container_def_with_network_isolation(time, sagemaker_session):
             "SAGEMAKER_SUBMIT_DIRECTORY": "/opt/ml/model/code",
             "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
             "SAGEMAKER_REGION": REGION,
-            "SAGEMAKER_ENABLE_CLOUDWATCH_METRICS": "false",
         },
         "Image": MODEL_IMAGE,
         "ModelDataUrl": MODEL_DATA,
@@ -144,7 +142,6 @@ def test_prepare_container_def_no_model_defaults(sagemaker_session, tmpdir):
         source_dir="sd",
         env={"a": "a"},
         name="name",
-        enable_cloudwatch_metrics=True,
         container_log_level=55,
         code_location="s3://cb/cp",
     )
@@ -155,7 +152,6 @@ def test_prepare_container_def_no_model_defaults(sagemaker_session, tmpdir):
             "SAGEMAKER_SUBMIT_DIRECTORY": "s3://cb/cp/name/sourcedir.tar.gz",
             "SAGEMAKER_CONTAINER_LOG_LEVEL": "55",
             "SAGEMAKER_REGION": REGION,
-            "SAGEMAKER_ENABLE_CLOUDWATCH_METRICS": "true",
             "a": "a",
         },
         "Image": MODEL_IMAGE,

tests/unit/sagemaker/tensorflow/test_estimator.py

@@ -88,7 +88,6 @@ def _hyperparameters(horovod=False):
         "sagemaker_submit_directory": json.dumps(
             "s3://{}/{}/source/sourcedir.tar.gz".format(BUCKET_NAME, JOB_NAME)
         ),
-        "sagemaker_enable_cloudwatch_metrics": "false",
         "sagemaker_container_log_level": str(logging.INFO),
         "sagemaker_job_name": json.dumps(JOB_NAME),
         "sagemaker_region": json.dumps("us-west-2"),
@@ -220,7 +219,6 @@ def test_create_model_with_optional_params(
 
     container_log_level = '"logging.INFO"'
     source_dir = "s3://mybucket/source"
-    enable_cloudwatch_metrics = "true"
     tf = TensorFlow(
         entry_point=SCRIPT_PATH,
         framework_version=tensorflow_inference_version,
@@ -232,7 +230,6 @@ def test_create_model_with_optional_params(
         container_log_level=container_log_level,
         base_job_name="job",
         source_dir=source_dir,
-        enable_cloudwatch_metrics=enable_cloudwatch_metrics,
         output_path="s3://mybucket/output",
     )