test rtd

src/sagemaker/estimator.py

@@ -31,6 +31,7 @@
 from sagemaker.debugger import TensorBoardOutputConfig  # noqa: F401 # pylint: disable=unused-import
 from sagemaker.debugger import get_rule_container_image_uri
 from sagemaker.s3 import S3Uploader
+from sagemaker.sdk_metrics import SDKMetrics
 
 from sagemaker.fw_utils import (
     create_image_uri,
@@ -493,7 +494,8 @@ def fit(self, inputs=None, wait=True, logs="All", job_name=None, experiment_conf
         """
         self._prepare_for_training(job_name=job_name)
 
-        self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config)
+        self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config,
+                                                          SDKMetrics(__name__, "fit"))
         self.jobs.append(self.latest_training_job)
         if wait:
             self.latest_training_job.wait(logs=logs)
@@ -988,7 +990,7 @@ class _TrainingJob(_Job):
     """Placeholder docstring"""
 
     @classmethod
-    def start_new(cls, estimator, inputs, experiment_config):
+    def start_new(cls, estimator, inputs, experiment_config, sdk_metrics=None):
         """Create a new Amazon SageMaker training job from the estimator.
 
         Args:
@@ -1065,7 +1067,7 @@ def start_new(cls, estimator, inputs, experiment_config):
         if estimator.enable_sagemaker_metrics is not None:
             train_args["enable_sagemaker_metrics"] = estimator.enable_sagemaker_metrics
 
-        estimator.sagemaker_session.train(**train_args)
+        estimator.sagemaker_session.train(**train_args, sdk_metrics=sdk_metrics)
 
         return cls(estimator.sagemaker_session, estimator._current_job_name)
 

src/sagemaker/sdk_metrics.py

@@ -0,0 +1,32 @@
+# Copyright 2020 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.
+
+from datetime import datetime
+
+
+class SDKMetrics(object):
+    def __init__(self, module, function=None):
+        self.module = module
+        self.function = function
+        self.timestamp = datetime.now()
+
+    def get_metrics_string(self):
+        metrics = "MODULE/{}, DURATION/{}".format(
+            self.module,
+            (datetime.now() - self.timestamp).microseconds
+        )
+
+        if self.function:
+            metrics = "FUNCTION/{} {}".format(self.function, metrics)
+
+        return metrics

src/sagemaker/session.py

@@ -31,7 +31,7 @@
 
 # import s3_input for backward compatibility
 from sagemaker.inputs import s3_input  # noqa # pylint: disable=unused-import
-from sagemaker.user_agent import prepend_user_agent
+from sagemaker.user_agent import prepend_user_agent, update_sdk_metrics
 from sagemaker.utils import (
     name_from_image,
     secondary_training_status_changed,
@@ -132,7 +132,7 @@ def _initialize(self, boto_session, sagemaker_client, sagemaker_runtime_client):
             )
 
         self.sagemaker_client = sagemaker_client or self.boto_session.client("sagemaker")
-        prepend_user_agent(self.sagemaker_client)
+        self._sagemaker_client_default_user_agent = prepend_user_agent(self.sagemaker_client)
 
         if sagemaker_runtime_client is not None:
             self.sagemaker_runtime_client = sagemaker_runtime_client
@@ -142,10 +142,28 @@ def _initialize(self, boto_session, sagemaker_client, sagemaker_runtime_client):
                 "runtime.sagemaker", config=config
             )
 
-        prepend_user_agent(self.sagemaker_runtime_client)
+        self._sagemaker_runtime_client_default_user_agent = prepend_user_agent(
+            self.sagemaker_runtime_client
+        )
 
         self.local_mode = False
 
+    def sagemaker_client_sdk_metrics(self, sdk_metrics):
+        """
+            sdk_metrics (sagemaker.SDKMetrics): An object that defines
+                the Python SDK telemetry metrics used to track Python SDK usage
+        Returns:
+            boto3.SageMaker.Client: Client which makes Amazon SageMaker service
+                calls other than ``InvokeEndpoint`` with updated user_agent string
+                with sdk_metrics.
+        """
+        update_sdk_metrics(
+            self.sagemaker_client,
+            self._sagemaker_client_default_user_agent,
+            sdk_metrics
+        )
+        return self.sagemaker_client
+
     @property
     def boto_region_name(self):
         """Placeholder docstring"""
@@ -440,6 +458,7 @@ def train(  # noqa: C901
         debugger_hook_config=None,
         tensorboard_output_config=None,
         enable_sagemaker_metrics=None,
+        sdk_metrics=None,
     ):
         """Create an Amazon SageMaker training job.
 
@@ -509,7 +528,6 @@ def train(  # noqa: C901
                 Series. For more information see:
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries
                 (default: ``None``).
-
         Returns:
             str: ARN of the training job, if it is created.
         """
@@ -587,7 +605,9 @@ def train(  # noqa: C901
 
         LOGGER.info("Creating training-job with name: %s", job_name)
         LOGGER.debug("train request: %s", json.dumps(train_request, indent=4))
-        self.sagemaker_client.create_training_job(**train_request)
+
+        self.sagemaker_client_sdk_metrics(sdk_metrics=sdk_metrics)\
+            .create_training_job(**train_request)
 
     def process(
         self,
@@ -664,7 +684,7 @@ def process(
 
         LOGGER.info("Creating processing-job with name %s", job_name)
         LOGGER.debug("process request: %s", json.dumps(process_request, indent=4))
-        self.sagemaker_client.create_processing_job(**process_request)
+        self.sagemaker_client_sdk_metrics([]).create_processing_job(**process_request)
 
     def create_monitoring_schedule(
         self,

src/sagemaker/user_agent.py

@@ -52,11 +52,32 @@ def determine_prefix(user_agent=""):
 def prepend_user_agent(client):
     """
     Args:
-        client:
+        client: boto3 client (e.g. sagemaker or sagemaker-runtime client)
+    Returns:
+        (str): Prepended user_agent string
     """
     prefix = determine_prefix(client._client_config.user_agent)
 
     if client._client_config.user_agent is None:
         client._client_config.user_agent = prefix
     else:
         client._client_config.user_agent = "{} {}".format(prefix, client._client_config.user_agent)
+
+    return client._client_config.user_agent
+
+
+def update_sdk_metrics(client, default_user_agent, sdk_metrics):
+    """
+    Args:
+        client (boto3.*.Client): boto3 client (e.g. boto3.SageMaker.Client or
+            boto3.SageMakerRuntime.Client client)
+        default_user_agent (str): default user agent string without any call specific SDK metrics
+        sdk_metrics (sagemaker.SDKMetrics): an object of SDKMetrics specific to a call
+    """
+    try:
+        client._client_config.user_agent = "{} {}".format(
+            default_user_agent, sdk_metrics.get_metrics_string()
+        )
+    except:
+        # Any telemetry is secondary and we DO want to silently ignore failures.
+        pass