make fixture for training_job_description

Author: Chuyang Deng

src/sagemaker/estimator.py

@@ -589,14 +589,16 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name="m
         has a Complete status, it can be ``deploy()`` ed to create a SageMaker
         Endpoint and return a ``Predictor``.
 
-        If the training job is in progress, attach will block and display log
-        messages from the training job, until the training job completes.
+        If the training job is in progress, attach will block until the training job
+        completes, but logs of the training job will not display. To see the logs
+        content, please call ``logs()``
 
         Examples:
             >>> my_estimator.fit(wait=False)
             >>> training_job_name = my_estimator.latest_training_job.name
             Later on:
             >>> attached_estimator = Estimator.attach(training_job_name)
+            >>> attached_estimator.logs()
             >>> attached_estimator.deploy()
 
         Args:
@@ -634,8 +636,10 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name="m
         return estimator
 
     def logs(self):
-        """Display the logs for Estimator's training job. If the output is a tty or a Jupyter
-        cell, it will be color-coded based on which instance the log entry is from.
+        """Display the logs for Estimator's training job.
+
+        If the output is a tty or a Jupyter cell, it will be color-coded based
+        on which instance the log entry is from.
         """
         self.sagemaker_session.logs_for_job(self.latest_training_job, wait=True)
 
@@ -1837,14 +1841,16 @@ def attach(cls, training_job_name, sagemaker_session=None, model_channel_name="m
         has a Complete status, it can be ``deploy()`` ed to create a SageMaker
         Endpoint and return a ``Predictor``.
 
-        If the training job is in progress, attach will block and display log
-        messages from the training job, until the training job completes.
+        If the training job is in progress, attach will block until the training job
+        completes, but logs of the training job will not display. To see the logs
+        content, please call ``logs()``
 
         Examples:
             >>> my_estimator.fit(wait=False)
             >>> training_job_name = my_estimator.latest_training_job.name
             Later on:
             >>> attached_estimator = Estimator.attach(training_job_name)
+            >>> attached_estimator.logs()
             >>> attached_estimator.deploy()
 
         Args:

tests/unit/test_estimator.py

@@ -191,6 +191,16 @@ def sagemaker_session():
     return sms
 
 
+@pytest.fixture()
+def training_job_description(sagemaker_session):
+    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
+    mock_describe_training_job = Mock(
+        name="describe_training_job", return_value=returned_job_description
+    )
+    sagemaker_session.sagemaker_client.describe_training_job = mock_describe_training_job
+    return returned_job_description
+
+
 def test_framework_all_init_args(sagemaker_session):
     f = DummyFramework(
         "my_script.py",
@@ -651,13 +661,9 @@ def test_enable_cloudwatch_metrics(sagemaker_session):
     assert train_kwargs["hyperparameters"]["sagemaker_enable_cloudwatch_metrics"]
 
 
-def test_attach_framework(sagemaker_session):
-    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
-    returned_job_description["VpcConfig"] = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]}
-    returned_job_description["EnableNetworkIsolation"] = True
-    sagemaker_session.sagemaker_client.describe_training_job = Mock(
-        name="describe_training_job", return_value=returned_job_description
-    )
+def test_attach_framework(sagemaker_session, training_job_description):
+    training_job_description["VpcConfig"] = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]}
+    training_job_description["EnableNetworkIsolation"] = True
 
     framework_estimator = DummyFramework.attach(
         training_job_name="neo", sagemaker_session=sagemaker_session
@@ -681,50 +687,25 @@ def test_attach_framework(sagemaker_session):
     assert framework_estimator.enable_network_isolation() is True
 
 
-def test_attach_no_logs(sagemaker_session):
-    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
-    mock_describe_training_job = Mock(
-        name="describe_training_job", return_value=returned_job_description
-    )
-    sagemaker_session.sagemaker_client.describe_training_job = mock_describe_training_job
+def test_attach_no_logs(sagemaker_session, training_job_description):
     Estimator.attach(training_job_name="job", sagemaker_session=sagemaker_session)
     sagemaker_session.logs_for_job.assert_not_called()
 
 
-def test_logs(sagemaker_session):
-    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
-    mock_describe_training_job = Mock(
-        name="describe_training_job", return_value=returned_job_description
-    )
-    sagemaker_session.sagemaker_client.describe_training_job = mock_describe_training_job
+def test_logs(sagemaker_session, training_job_description):
     estimator = Estimator.attach(training_job_name="job", sagemaker_session=sagemaker_session)
     estimator.logs()
     sagemaker_session.logs_for_job.assert_called_with(estimator.latest_training_job, wait=True)
 
 
-def test_attach_without_hyperparameters(sagemaker_session):
-    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
-    del returned_job_description["HyperParameters"]
-
-    mock_describe_training_job = Mock(
-        name="describe_training_job", return_value=returned_job_description
-    )
-    sagemaker_session.sagemaker_client.describe_training_job = mock_describe_training_job
-
+def test_attach_without_hyperparameters(sagemaker_session, training_job_description):
+    del training_job_description["HyperParameters"]
     estimator = Estimator.attach(training_job_name="job", sagemaker_session=sagemaker_session)
-
     assert estimator.hyperparameters() == {}
 
 
-def test_attach_framework_with_tuning(sagemaker_session):
-    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
-    returned_job_description["HyperParameters"]["_tuning_objective_metric"] = "Validation-accuracy"
-
-    mock_describe_training_job = Mock(
-        name="describe_training_job", return_value=returned_job_description
-    )
-    sagemaker_session.sagemaker_client.describe_training_job = mock_describe_training_job
-
+def test_attach_framework_with_tuning(sagemaker_session, training_job_description):
+    training_job_description["HyperParameters"]["_tuning_objective_metric"] = "Validation-accuracy"
     framework_estimator = DummyFramework.attach(
         training_job_name="neo", sagemaker_session=sagemaker_session
     )
@@ -744,48 +725,35 @@ def test_attach_framework_with_tuning(sagemaker_session):
     assert framework_estimator.encrypt_inter_container_traffic is False
 
 
-def test_attach_framework_with_model_channel(sagemaker_session):
+def test_attach_framework_with_model_channel(sagemaker_session, training_job_description):
     s3_uri = "s3://some/s3/path/model.tar.gz"
-    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
-    returned_job_description["InputDataConfig"] = [
+    training_job_description["InputDataConfig"] = [
         {
             "ChannelName": "model",
             "InputMode": "File",
             "DataSource": {"S3DataSource": {"S3Uri": s3_uri}},
         }
     ]
 
-    sagemaker_session.sagemaker_client.describe_training_job = Mock(
-        name="describe_training_job", return_value=returned_job_description
-    )
-
     framework_estimator = DummyFramework.attach(
         training_job_name="neo", sagemaker_session=sagemaker_session
     )
     assert framework_estimator.model_uri is s3_uri
     assert framework_estimator.encrypt_inter_container_traffic is False
 
 
-def test_attach_framework_with_inter_container_traffic_encryption_flag(sagemaker_session):
-    returned_job_description = RETURNED_JOB_DESCRIPTION.copy()
-    returned_job_description["EnableInterContainerTrafficEncryption"] = True
-
-    sagemaker_session.sagemaker_client.describe_training_job = Mock(
-        name="describe_training_job", return_value=returned_job_description
-    )
-
+def test_attach_framework_with_inter_container_traffic_encryption_flag(
+    sagemaker_session, training_job_description
+):
+    training_job_description["EnableInterContainerTrafficEncryption"] = True
     framework_estimator = DummyFramework.attach(
         training_job_name="neo", sagemaker_session=sagemaker_session
     )
 
     assert framework_estimator.encrypt_inter_container_traffic is True
 
 
-def test_attach_framework_base_from_generated_name(sagemaker_session):
-    sagemaker_session.sagemaker_client.describe_training_job = Mock(
-        name="describe_training_job", return_value=RETURNED_JOB_DESCRIPTION
-    )
-
+def test_attach_framework_base_from_generated_name(sagemaker_session, training_job_description):
     base_job_name = "neo"
     framework_estimator = DummyFramework.attach(
         training_job_name=utils.name_from_base("neo"), sagemaker_session=sagemaker_session