breaking: default wait=True for HyperparameterTuner.fit() and Transformer.transform() (#1790)

Author: laurenyu

src/sagemaker/transformer.py

@@ -121,8 +121,8 @@ def transform(
         join_source=None,
         experiment_config=None,
         model_client_config=None,
-        wait=False,
-        logs=False,
+        wait=True,
+        logs=True,
     ):
         """Start a new transform job.
 
@@ -178,9 +178,9 @@ def transform(
                 'InvocationsTimeoutInSeconds', and 'InvocationsMaxRetries'.
                 (default: ``None``).
             wait (bool): Whether the call should wait until the job completes
-                (default: False).
+                (default: ``True``).
             logs (bool): Whether to show the logs produced by the job.
-                Only meaningful when wait is True (default: False).
+                Only meaningful when wait is ``True`` (default: ``True``).
         """
         local_mode = self.sagemaker_session.local_mode
         if not local_mode and not data.startswith("s3://"):

src/sagemaker/tuner.py

@@ -369,6 +369,7 @@ def fit(
         job_name=None,
         include_cls_metadata=False,
         estimator_kwargs=None,
+        wait=True,
         **kwargs
     ):
         """Start a hyperparameter tuning job.
@@ -424,6 +425,7 @@ def fit(
                 The keys are the estimator names for the estimator_dict argument of create()
                 method. Each value is a dictionary for the other arguments needed for training
                 of the corresponding estimator.
+            wait (bool): Whether the call should wait until the job completes (default: ``True``).
             **kwargs: Other arguments needed for training. Please refer to the
                 ``fit()`` method of the associated estimator to see what other
                 arguments are needed.
@@ -433,6 +435,9 @@ def fit(
         else:
             self._fit_with_estimator_dict(inputs, job_name, include_cls_metadata, estimator_kwargs)
 
+        if wait:
+            self.latest_tuning_job.wait()
+
     def _fit_with_estimator(self, inputs, job_name, include_cls_metadata, **kwargs):
         """Start tuning for tuner instances that have the ``estimator`` field set"""
         self._prepare_estimator_for_tuning(self.estimator, inputs, job_name, **kwargs)
@@ -1447,9 +1452,6 @@ def start_new(cls, tuner, inputs):
             sagemaker.tuner._TuningJob: Constructed object that captures all
             information about the started job.
         """
-
-        logger.info("_TuningJob.start_new!!!")
-
         warm_start_config_req = None
         if tuner.warm_start_config:
             warm_start_config_req = tuner.warm_start_config.to_input_req()

tests/integ/test_transformer.py

@@ -325,7 +325,7 @@ def test_single_transformer_multiple_jobs(
 
 def test_stop_transform_job(mxnet_estimator, mxnet_transform_input, cpu_instance_type):
     transformer = mxnet_estimator.transformer(1, cpu_instance_type)
-    transformer.transform(mxnet_transform_input, content_type="text/csv")
+    transformer.transform(mxnet_transform_input, content_type="text/csv", wait=False)
 
     time.sleep(15)
 

tests/integ/test_tuner.py

@@ -130,7 +130,7 @@ def _tune(
     hyperparameter_ranges=None,
     job_name=None,
     warm_start_config=None,
-    wait_till_terminal=True,
+    wait=True,
     max_jobs=2,
     max_parallel_jobs=2,
     early_stopping_type="Off",
@@ -152,11 +152,8 @@ def _tune(
         records = kmeans_estimator.record_set(kmeans_train_set[0][:100])
         test_record_set = kmeans_estimator.record_set(kmeans_train_set[0][:100], channel="test")
 
-        tuner.fit([records, test_record_set], job_name=job_name)
-        print("Started hyperparameter tuning job with name:" + tuner.latest_tuning_job.name)
-
-        if wait_till_terminal:
-            tuner.wait()
+        print("Started hyperparameter tuning job with name: {}".format(job_name))
+        tuner.fit([records, test_record_set], job_name=job_name, wait=wait)
 
     return tuner
 
@@ -388,7 +385,7 @@ def test_tuning_kmeans_identical_dataset_algorithm_tuner_from_non_terminal_paren
         kmeans_train_set,
         job_name=parent_tuning_job_name,
         hyperparameter_ranges=hyperparameter_ranges,
-        wait_till_terminal=False,
+        wait=False,
         max_parallel_jobs=1,
         max_jobs=1,
     )
@@ -453,15 +450,9 @@ def test_tuning_lda(sagemaker_session, cpu_instance_type):
         )
 
         tuning_job_name = unique_name_from_base("test-lda", max_length=32)
+        print("Started hyperparameter tuning job with name:" + tuning_job_name)
         tuner.fit([record_set, test_record_set], mini_batch_size=1, job_name=tuning_job_name)
 
-        latest_tuning_job_name = tuner.latest_tuning_job.name
-
-        print("Started hyperparameter tuning job with name:" + latest_tuning_job_name)
-
-        time.sleep(15)
-        tuner.wait()
-
     attached_tuner = HyperparameterTuner.attach(
         tuning_job_name, sagemaker_session=sagemaker_session
     )
@@ -516,7 +507,7 @@ def test_stop_tuning_job(sagemaker_session, cpu_instance_type):
     )
 
     tuning_job_name = unique_name_from_base("test-randomcutforest", max_length=32)
-    tuner.fit([records, test_records], tuning_job_name)
+    tuner.fit([records, test_records], tuning_job_name, wait=False)
 
     time.sleep(15)
 
@@ -575,12 +566,8 @@ def test_tuning_mxnet(
         )
 
         tuning_job_name = unique_name_from_base("tune-mxnet", max_length=32)
-        tuner.fit({"train": train_input, "test": test_input}, job_name=tuning_job_name)
-
         print("Started hyperparameter tuning job with name:" + tuning_job_name)
-
-        time.sleep(15)
-        tuner.wait()
+        tuner.fit({"train": train_input, "test": test_input}, job_name=tuning_job_name)
 
     best_training_job = tuner.best_training_job()
     with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
@@ -628,12 +615,8 @@ def test_tuning_tf(
         )
 
         tuning_job_name = unique_name_from_base("tune-tf", max_length=32)
-        tuner.fit(inputs, job_name=tuning_job_name)
-
         print("Started hyperparameter tuning job with name: " + tuning_job_name)
-
-        time.sleep(15)
-        tuner.wait()
+        tuner.fit(inputs, job_name=tuning_job_name)
 
 
 def test_tuning_tf_vpc_multi(
@@ -686,12 +669,8 @@ def test_tuning_tf_vpc_multi(
         )
 
         tuning_job_name = unique_name_from_base("tune-tf", max_length=32)
-        tuner.fit(inputs, job_name=tuning_job_name)
-
         print(f"Started hyperparameter tuning job with name: {tuning_job_name}")
-
-        time.sleep(15)
-        tuner.wait()
+        tuner.fit(inputs, job_name=tuning_job_name)
 
 
 @pytest.mark.canary_quick
@@ -740,13 +719,9 @@ def test_tuning_chainer(
         )
 
         tuning_job_name = unique_name_from_base("chainer", max_length=32)
+        print("Started hyperparameter tuning job with name: {}".format(tuning_job_name))
         tuner.fit({"train": train_input, "test": test_input}, job_name=tuning_job_name)
 
-        print("Started hyperparameter tuning job with name:" + tuning_job_name)
-
-        time.sleep(15)
-        tuner.wait()
-
     best_training_job = tuner.best_training_job()
     with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
         predictor = tuner.deploy(1, cpu_instance_type)
@@ -812,13 +787,9 @@ def test_attach_tuning_pytorch(
         )
 
         tuning_job_name = unique_name_from_base("pytorch", max_length=32)
+        print("Started hyperparameter tuning job with name: {}".format(tuning_job_name))
         tuner.fit({"training": training_data}, job_name=tuning_job_name)
 
-        print("Started hyperparameter tuning job with name:" + tuning_job_name)
-
-        time.sleep(15)
-        tuner.wait()
-
     endpoint_name = tuning_job_name
     model_name = "model-name-1"
     attached_tuner = HyperparameterTuner.attach(
@@ -887,17 +858,14 @@ def test_tuning_byo_estimator(sagemaker_session, cpu_instance_type):
             max_parallel_jobs=2,
         )
 
+        tuning_job_name = unique_name_from_base("byo", 32)
+        print("Started hyperparameter tuning job with name {}:".format(tuning_job_name))
         tuner.fit(
             {"train": s3_train_data, "test": s3_train_data},
             include_cls_metadata=False,
-            job_name=unique_name_from_base("byo", 32),
+            job_name=tuning_job_name,
         )
 
-        print("Started hyperparameter tuning job with name:" + tuner.latest_tuning_job.name)
-
-        time.sleep(15)
-        tuner.wait()
-
     best_training_job = tuner.best_training_job()
     with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
         predictor = tuner.deploy(