Make the metric_names list optional in TrainingJobAnalytics object.

CHANGELOG.rst

@@ -5,6 +5,7 @@ CHANGELOG
 1.5.3dev
 ========
 
+* bug-fix: Can create TrainingJobAnalytics object without specifying metric_names.
 * bug-fix: Session: include role path in ``get_execution_role()`` result
 
 1.5.2

src/sagemaker/analytics.py

@@ -20,7 +20,7 @@
 from six import with_metaclass
 
 from sagemaker.session import Session
-from sagemaker.utils import DeferredError
+from sagemaker.utils import DeferredError, extract_name_from_job_arn
 
 try:
     import pandas as pd
@@ -201,12 +201,13 @@ class TrainingJobAnalytics(AnalyticsMetricsBase):
 
     CLOUDWATCH_NAMESPACE = '/aws/sagemaker/HyperParameterTuningJobs'
 
-    def __init__(self, training_job_name, metric_names, sagemaker_session=None):
+    def __init__(self, training_job_name, metric_names=None, sagemaker_session=None):
         """Initialize a ``TrainingJobAnalytics`` instance.
 
         Args:
             training_job_name (str): name of the TrainingJob to analyze.
-            metric_names (list): string names of all the metrics to collect for this training job
+            metric_names (list, optional): string names of all the metrics to collect for this training job.
+                If not specified, then it will use all metric names configured for this job.
             sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
                 Amazon SageMaker APIs and any other AWS services needed. If not specified, one is specified
                 using the default AWS configuration chain.
@@ -215,7 +216,10 @@ def __init__(self, training_job_name, metric_names, sagemaker_session=None):
         self._sage_client = sagemaker_session.sagemaker_client
         self._cloudwatch = sagemaker_session.boto_session.client('cloudwatch')
         self._training_job_name = training_job_name
-        self._metric_names = metric_names
+        if metric_names:
+            self._metric_names = metric_names
+        else:
+            self._metric_names = self._metric_names_for_training_job()
         self.clear_cache()
 
     @property
@@ -297,3 +301,22 @@ def _add_single_metric(self, timestamp, metric_name, value):
         self._data['timestamp'].append(timestamp)
         self._data['metric_name'].append(metric_name)
         self._data['value'].append(value)
+
+    def _metric_names_for_training_job(self):
+        """Helper method to discover the metrics defined for a training job.
+        """
+        # First look up the tuning job
+        training_description = self._sage_client.describe_training_job(TrainingJobName=self._training_job_name)
+        tuning_job_arn = training_description.get('TuningJobArn', None)
+        if not tuning_job_arn:
+            raise ValueError(
+                "No metrics available. Training Job Analytics only available through Hyperparameter Tuning Jobs"
+            )
+        tuning_job_name = extract_name_from_job_arn(tuning_job_arn)
+        tuning_job_description = self._sage_client.describe_hyper_parameter_tuning_job(
+            HyperParameterTuningJobName=tuning_job_name
+        )
+        training_job_definition = tuning_job_description['TrainingJobDefinition']
+        metric_definitions = training_job_definition['AlgorithmSpecification']['MetricDefinitions']
+        metric_names = [md['Name'] for md in metric_definitions]
+        return metric_names

src/sagemaker/estimator.py

@@ -324,7 +324,7 @@ def training_job_analytics(self):
         """
         if self._current_job_name is None:
             raise ValueError('Estimator is not associated with a TrainingJob')
-        return TrainingJobAnalytics(self._current_job_name)
+        return TrainingJobAnalytics(self._current_job_name, sagemaker_session=self.sagemaker_session)
 
 
 class _TrainingJob(_Job):

src/sagemaker/utils.py

@@ -120,6 +120,16 @@ def to_str(value):
         return str(value)
 
 
+def extract_name_from_job_arn(arn):
+    """Returns the name used in the API given a full ARN for a training job
+    or hyperparameter tuning job.
+    """
+    slash_pos = arn.find('/')
+    if slash_pos == -1:
+        raise ValueError("Cannot parse invalid ARN: %s" % arn)
+    return arn[(slash_pos + 1):]
+
+
 class DeferredError(object):
     """Stores an exception and raises it at a later time anytime this
     object is accessed in any way.  Useful to allow soft-dependencies on imports,

tests/unit/test_estimator.py

@@ -594,6 +594,47 @@ def test_generic_to_deploy(sagemaker_session):
     assert predictor.sagemaker_session == sagemaker_session
 
 
+def test_generic_training_job_analytics(sagemaker_session):
+    sagemaker_session.sagemaker_client.describe_training_job = Mock(name='describe_training_job', return_value={
+        'TuningJobArn': 'arn:aws:sagemaker:us-west-2:968277160000:hyper-parameter-tuning-job/mock-tuner',
+        'TrainingStartTime': 1530562991.299,
+    })
+    sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job = Mock(
+        name='describe_hyper_parameter_tuning_job',
+        return_value={
+            'TrainingJobDefinition': {
+                "AlgorithmSpecification": {
+                    "TrainingImage": "some-image-url",
+                    "TrainingInputMode": "File",
+                    "MetricDefinitions": [
+                        {
+                            "Name": "train:loss",
+                            "Regex": "train_loss=([0-9]+\\.[0-9]+)"
+                        },
+                        {
+                            "Name": "validation:loss",
+                            "Regex": "valid_loss=([0-9]+\\.[0-9]+)"
+                        }
+                    ]
+                }
+            }
+        }
+    )
+
+    e = Estimator(IMAGE_NAME, ROLE, INSTANCE_COUNT, INSTANCE_TYPE, output_path='s3://bucket/prefix',
+                  sagemaker_session=sagemaker_session)
+
+    with pytest.raises(ValueError) as err:  # noqa: F841
+        # No training job yet
+        a = e.training_job_analytics
+        assert a is not None  # This line is never reached
+
+    e.set_hyperparameters(**HYPERPARAMS)
+    e.fit({'train': 's3://bucket/training-prefix'})
+    a = e.training_job_analytics
+    assert a is not None
+
+
 @patch('sagemaker.estimator.LocalSession')
 @patch('sagemaker.estimator.Session')
 def test_local_mode(session_class, local_session_class):

tests/unit/test_utils.py

@@ -17,7 +17,7 @@
 import pytest
 from mock import patch
 
-from sagemaker.utils import get_config_value, name_from_base, to_str, DeferredError
+from sagemaker.utils import get_config_value, name_from_base, to_str, DeferredError, extract_name_from_job_arn
 
 NAME = 'base_name'
 
@@ -77,3 +77,15 @@ def test_to_str_with_native_string():
 def test_to_str_with_unicode_string():
     value = u'åñøthér strîng'
     assert to_str(value) == value
+
+
+def test_name_from_tuning_arn():
+    arn = 'arn:aws:sagemaker:us-west-2:968277160000:hyper-parameter-tuning-job/resnet-sgd-tuningjob-11-07-34-11'
+    name = extract_name_from_job_arn(arn)
+    assert name == 'resnet-sgd-tuningjob-11-07-34-11'
+
+
+def test_name_from_training_arn():
+    arn = 'arn:aws:sagemaker:us-west-2:968277160000:training-job/resnet-sgd-tuningjob-11-22-38-46-002-2927640b'
+    name = extract_name_from_job_arn(arn)
+    assert name == 'resnet-sgd-tuningjob-11-22-38-46-002-2927640b'