breaking: rename estimator.train_image() to estimator.training_image_uri()

src/sagemaker/algorithm.py

@@ -229,13 +229,13 @@ def hyperparameters(self):
         """
         return self.hyperparam_dict
 
-    def train_image(self):
+    def training_image_uri(self):
         """Returns the docker image to use for training.
 
         The fit() method, that does the model training, calls this method to
         find the image to use for model training.
         """
-        raise RuntimeError("train_image is never meant to be called on Algorithm Estimators")
+        raise RuntimeError("training_image_uri is never meant to be called on Algorithm Estimators")
 
     def enable_network_isolation(self):
         """Return True if this Estimator will need network isolation to run.

src/sagemaker/amazon/amazon_estimator.py

@@ -91,7 +91,7 @@ def __init__(
         )
         self._data_location = data_location
 
-    def train_image(self):
+    def training_image_uri(self):
         """Placeholder docstring"""
         return image_uris.retrieve(
             self.repo_name, self.sagemaker_session.boto_region_name, version=self.repo_version,

src/sagemaker/estimator.py

@@ -285,7 +285,7 @@ def __init__(
         self._enable_network_isolation = enable_network_isolation
 
     @abstractmethod
-    def train_image(self):
+    def training_image_uri(self):
         """Return the Docker image to use for training.
 
         The :meth:`~sagemaker.estimator.EstimatorBase.fit` method, which does
@@ -329,7 +329,7 @@ def _ensure_base_job_name(self):
         """Set ``self.base_job_name`` if it is not set already."""
         # honor supplied base_job_name or generate it
         if self.base_job_name is None:
-            self.base_job_name = base_name_from_image(self.train_image())
+            self.base_job_name = base_name_from_image(self.training_image_uri())
 
     def _get_or_create_name(self, name=None):
         """Generate a name based on the base job name or training image if needed.
@@ -507,7 +507,7 @@ def fit(self, inputs=None, wait=True, logs="All", job_name=None, experiment_conf
 
     def _compilation_job_name(self):
         """Placeholder docstring"""
-        base_name = self.base_job_name or base_name_from_image(self.train_image())
+        base_name = self.base_job_name or base_name_from_image(self.training_image_uri())
         return name_from_base("compilation-" + base_name)
 
     def compile_model(
@@ -1083,7 +1083,7 @@ def start_new(cls, estimator, inputs, experiment_config):
         if isinstance(estimator, sagemaker.algorithm.AlgorithmEstimator):
             train_args["algorithm_arn"] = estimator.algorithm_arn
         else:
-            train_args["image_uri"] = estimator.train_image()
+            train_args["image_uri"] = estimator.training_image_uri()
 
         if estimator.debugger_rule_configs:
             train_args["debugger_rule_configs"] = estimator.debugger_rule_configs
@@ -1350,7 +1350,7 @@ def __init__(
             enable_network_isolation=enable_network_isolation,
         )
 
-    def train_image(self):
+    def training_image_uri(self):
         """Returns the docker image to use for training.
 
         The fit() method, that does the model training, calls this method to
@@ -1424,7 +1424,7 @@ def predict_wrapper(endpoint, session):
             kwargs["enable_network_isolation"] = self.enable_network_isolation()
 
         return Model(
-            image_uri or self.train_image(),
+            image_uri or self.training_image_uri(),
             self.model_data,
             role,
             vpc_config=self.get_vpc_config(vpc_config_override),
@@ -1826,7 +1826,7 @@ class constructor
 
         return init_params
 
-    def train_image(self):
+    def training_image_uri(self):
         """Return the Docker image to use for training.
 
         The :meth:`~sagemaker.estimator.EstimatorBase.fit` method, which does

src/sagemaker/rl/estimator.py

@@ -268,7 +268,7 @@ def create_model(
             "An unknown RLFramework enum was passed in. framework: {}".format(self.framework)
         )
 
-    def train_image(self):
+    def training_image_uri(self):
         """Return the Docker image to use for training.
 
         The :meth:`~sagemaker.estimator.EstimatorBase.fit` method, which does

src/sagemaker/tuner.py

@@ -317,7 +317,7 @@ def _prepare_job_name_for_tuning(self, job_name=None):
                 estimator = (
                     self.estimator or self.estimator_dict[sorted(self.estimator_dict.keys())[0]]
                 )
-                base_name = base_name_from_image(estimator.train_image())
+                base_name = base_name_from_image(estimator.training_image_uri())
             self._current_job_name = name_from_base(
                 base_name, max_length=self.TUNING_JOB_NAME_MAX_LENGTH, short=True
             )
@@ -1527,7 +1527,7 @@ def _prepare_training_config(
         if isinstance(estimator, sagemaker.algorithm.AlgorithmEstimator):
             training_config["algorithm_arn"] = estimator.algorithm_arn
         else:
-            training_config["image_uri"] = estimator.train_image()
+            training_config["image_uri"] = estimator.training_image_uri()
 
         training_config["enable_network_isolation"] = estimator.enable_network_isolation()
         training_config[

src/sagemaker/workflow/airflow.py

@@ -149,7 +149,9 @@ def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=
     if job_name is not None:
         estimator._current_job_name = job_name
     else:
-        base_name = estimator.base_job_name or utils.base_name_from_image(estimator.train_image())
+        base_name = estimator.base_job_name or utils.base_name_from_image(
+            estimator.training_image_uri()
+        )
         estimator._current_job_name = utils.name_from_base(base_name)
 
     if estimator.output_path is None:
@@ -164,7 +166,7 @@ def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=
 
     train_config = {
         "AlgorithmSpecification": {
-            "TrainingImage": estimator.train_image(),
+            "TrainingImage": estimator.training_image_uri(),
             "TrainingInputMode": estimator.input_mode,
         },
         "OutputDataConfig": job_config["output_config"],

tests/integ/test_byo_estimator.py

@@ -151,4 +151,4 @@ def test_async_byo_estimator(sagemaker_session, region, cpu_instance_type, train
         for prediction in result["predictions"]:
             assert prediction["score"] is not None
 
-        assert estimator.train_image() == image_uri
+        assert estimator.training_image_uri() == image_uri

tests/unit/sagemaker/tensorflow/test_estimator.py

@@ -521,7 +521,7 @@ def test_hyperparameters_no_model_dir(
     assert "model_dir" not in hyperparameters
 
 
-def test_train_image_custom_image(sagemaker_session):
+def test_custom_image(sagemaker_session):
     custom_image = "tensorflow:latest"
     tf = _build_tf(sagemaker_session, image_uri=custom_image)
-    assert custom_image == tf.train_image()
+    assert custom_image == tf.training_image_uri()

tests/unit/sagemaker/tensorflow/test_estimator_attach.py

@@ -98,7 +98,7 @@ def test_attach(sagemaker_session, tensorflow_training_version, tensorflow_train
     assert estimator.hyperparameters() is not None
     assert estimator.source_dir == "s3://some/sourcedir.tar.gz"
     assert estimator.entry_point == "iris-dnn-classifier.py"
-    assert estimator.train_image() == training_image
+    assert estimator.training_image_uri() == training_image
 
 
 @patch("sagemaker.utils.create_tar_file", MagicMock())
@@ -207,4 +207,4 @@ def test_attach_custom_image(sagemaker_session):
 
     estimator = TensorFlow.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
     assert estimator.image_uri == training_image
-    assert estimator.train_image() == training_image
+    assert estimator.training_image_uri() == training_image

tests/unit/test_chainer.py

@@ -415,7 +415,7 @@ def test_model_prepare_container_def_no_instance_type_or_image(chainer_version,
     assert expected_msg in str(e)
 
 
-def test_train_image_default(sagemaker_session, chainer_version, chainer_py_version):
+def test_training_image_default(sagemaker_session, chainer_version, chainer_py_version):
     chainer = Chainer(
         entry_point=SCRIPT_PATH,
         role=ROLE,
@@ -426,7 +426,9 @@ def test_train_image_default(sagemaker_session, chainer_version, chainer_py_vers
         py_version=chainer_py_version,
     )
 
-    assert _get_full_cpu_image_uri(chainer_version, chainer_py_version) == chainer.train_image()
+    assert (
+        _get_full_cpu_image_uri(chainer_version, chainer_py_version) == chainer.training_image_uri()
+    )
 
 
 def test_attach(sagemaker_session, chainer_version, chainer_py_version):
@@ -545,7 +547,7 @@ def test_attach_custom_image(sagemaker_session):
 
     estimator = Chainer.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
     assert estimator.image_uri == training_image
-    assert estimator.train_image() == training_image
+    assert estimator.training_image_uri() == training_image
 
 
 @patch("sagemaker.chainer.estimator.python_deprecation_warning")

tests/unit/test_estimator.py

@@ -109,7 +109,7 @@
 class DummyFramework(Framework):
     _framework_name = "dummy"
 
-    def train_image(self):
+    def training_image_uri(self):
         return IMAGE_URI
 
     def create_model(

tests/unit/test_fm.py

@@ -147,7 +147,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     fm = FactorizationMachines(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("factorization-machines", REGION) == fm.train_image()
+    assert image_uris.retrieve("factorization-machines", REGION) == fm.training_image_uri()
 
 
 @pytest.mark.parametrize(

tests/unit/test_ipinsights.py

@@ -120,7 +120,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     ipinsights = IPInsights(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("ipinsights", REGION) == ipinsights.train_image()
+    assert image_uris.retrieve("ipinsights", REGION) == ipinsights.training_image_uri()
 
 
 @pytest.mark.parametrize(

tests/unit/test_job.py

@@ -83,7 +83,7 @@ def sagemaker_session():
 class DummyFramework(Framework):
     _framework_name = "dummy"
 
-    def train_image(self):
+    def training_image_uri(self):
         return IMAGE_NAME
 
     def create_model(self, role=None, model_server_workers=None):

tests/unit/test_kmeans.py

@@ -112,7 +112,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("kmeans", REGION) == kmeans.train_image()
+    assert image_uris.retrieve("kmeans", REGION) == kmeans.training_image_uri()
 
 
 @pytest.mark.parametrize("required_hyper_parameters, value", [("k", "string")])

tests/unit/test_knn.py

@@ -146,7 +146,7 @@ def test_all_hyperparameters_classifier(sagemaker_session):
 
 def test_image(sagemaker_session):
     knn = KNN(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("knn", REGION) == knn.train_image()
+    assert image_uris.retrieve("knn", REGION) == knn.training_image_uri()
 
 
 @pytest.mark.parametrize(

tests/unit/test_lda.py

@@ -96,7 +96,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("lda", REGION) == lda.train_image()
+    assert image_uris.retrieve("lda", REGION) == lda.training_image_uri()
 
 
 @pytest.mark.parametrize("required_hyper_parameters, value", [("num_topics", "string")])

tests/unit/test_linear_learner.py

@@ -179,7 +179,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("linear-learner", REGION) == lr.train_image()
+    assert image_uris.retrieve("linear-learner", REGION) == lr.training_image_uri()
 
 
 @pytest.mark.parametrize("required_hyper_parameters, value", [("predictor_type", 0)])

tests/unit/test_mxnet.py

@@ -670,7 +670,7 @@ def test_attach_custom_image(sagemaker_session):
 
     estimator = MXNet.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
     assert estimator.image_uri == training_image
-    assert estimator.train_image() == training_image
+    assert estimator.training_image_uri() == training_image
 
 
 def test_estimator_script_mode_dont_launch_parameter_server(sagemaker_session):

tests/unit/test_ntm.py

@@ -115,7 +115,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     ntm = NTM(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("ntm", REGION) == ntm.train_image()
+    assert image_uris.retrieve("ntm", REGION) == ntm.training_image_uri()
 
 
 @pytest.mark.parametrize("required_hyper_parameters, value", [("num_topics", "string")])

tests/unit/test_object2vec.py

@@ -144,7 +144,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     object2vec = Object2Vec(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("object2vec", REGION) == object2vec.train_image()
+    assert image_uris.retrieve("object2vec", REGION) == object2vec.training_image_uri()
 
 
 @pytest.mark.parametrize("required_hyper_parameters, value", [("epochs", "string")])

tests/unit/test_pca.py

@@ -101,7 +101,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     pca = PCA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("pca", REGION) == pca.train_image()
+    assert image_uris.retrieve("pca", REGION) == pca.training_image_uri()
 
 
 @pytest.mark.parametrize("required_hyper_parameters, value", [("num_components", "string")])

tests/unit/test_pytorch.py

@@ -537,7 +537,7 @@ def test_attach_custom_image(sagemaker_session):
     estimator = PyTorch.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
     assert estimator.latest_training_job.job_name == "neo"
     assert estimator.image_uri == training_image
-    assert estimator.train_image() == training_image
+    assert estimator.training_image_uri() == training_image
 
 
 @patch("sagemaker.pytorch.estimator.python_deprecation_warning")

tests/unit/test_randomcutforest.py

@@ -107,7 +107,7 @@ def test_all_hyperparameters(sagemaker_session):
 
 def test_image(sagemaker_session):
     randomcutforest = RandomCutForest(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
-    assert image_uris.retrieve("randomcutforest", REGION) == randomcutforest.train_image()
+    assert image_uris.retrieve("randomcutforest", REGION) == randomcutforest.training_image_uri()
 
 
 @pytest.mark.parametrize("iterable_hyper_parameters, value", [("eval_metrics", 0)])

tests/unit/test_rl.py

@@ -395,7 +395,7 @@ def test_deploy_ray(sagemaker_session, ray_tensorflow_version):
 
 
 @patch("sagemaker.image_uris.retrieve")
-def test_train_image(retrieve_image_uri, sagemaker_session, ray_tensorflow_version):
+def test_training_image_uri(retrieve_image_uri, sagemaker_session, ray_tensorflow_version):
     toolkit = RLToolkit.RAY
     framework = RLFramework.TENSORFLOW
 
@@ -408,13 +408,13 @@ def test_train_image(retrieve_image_uri, sagemaker_session, ray_tensorflow_versi
         instance_type=CPU,
         image_uri=image,
     )
-    assert image == rl.train_image()
+    assert image == rl.training_image_uri()
     retrieve_image_uri.assert_not_called()
 
     rl = _rl_estimator(
         sagemaker_session, toolkit, ray_tensorflow_version, framework, instance_type=CPU
     )
-    assert retrieve_image_uri.return_value == rl.train_image()
+    assert retrieve_image_uri.return_value == rl.training_image_uri()
 
     retrieve_image_uri.assert_called_with(
         "ray-tensorflow", REGION, version=ray_tensorflow_version, instance_type=CPU
@@ -540,7 +540,7 @@ def test_attach_custom_image(sagemaker_session):
     estimator = RLEstimator.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
     assert estimator.latest_training_job.job_name == "neo"
     assert estimator.image_uri == training_image
-    assert estimator.train_image() == training_image
+    assert estimator.training_image_uri() == training_image
 
 
 def test_wrong_framework_format(sagemaker_session):

tests/unit/test_sklearn.py

@@ -140,7 +140,7 @@ def _create_train_job(version):
     }
 
 
-def test_train_image(sagemaker_session, sklearn_version):
+def test_training_image_uri(sagemaker_session, sklearn_version):
     container_log_level = '"logging.INFO"'
     source_dir = "s3://mybucket/source"
     sklearn = SKLearn(
@@ -155,7 +155,7 @@ def test_train_image(sagemaker_session, sklearn_version):
         source_dir=source_dir,
     )
 
-    assert _get_full_cpu_image_uri(sklearn_version) == sklearn.train_image()
+    assert _get_full_cpu_image_uri(sklearn_version) == sklearn.training_image_uri()
 
 
 def test_create_model(sagemaker_session, sklearn_version):
@@ -525,7 +525,7 @@ def test_attach_custom_image(sagemaker_session):
 
     estimator = SKLearn.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
     assert estimator.image_uri == training_image
-    assert estimator.train_image() == training_image
+    assert estimator.training_image_uri() == training_image
 
 
 def test_estimator_py2_raises(sagemaker_session, sklearn_version):

tests/unit/test_tuner.py

@@ -386,7 +386,7 @@ def test_fit_multi_estimators(sagemaker_session):
     assert training_config_one["objective_type"] == "Minimize"
     assert training_config_one["objective_metric_name"] == OBJECTIVE_METRIC_NAME
     assert training_config_one["input_config"] is None
-    assert training_config_one["image_uri"] == estimator_one.train_image()
+    assert training_config_one["image_uri"] == estimator_one.training_image_uri()
     assert training_config_one["metric_definitions"] == METRIC_DEFINITIONS
     assert (
         training_config_one["static_hyperparameters"]["sagemaker_estimator_module"]
@@ -403,7 +403,7 @@ def test_fit_multi_estimators(sagemaker_session):
     assert training_config_two["objective_metric_name"] == OBJECTIVE_METRIC_NAME_TWO
     assert len(training_config_two["input_config"]) == 1
     assert training_config_two["input_config"][0]["DataSource"]["S3DataSource"]["S3Uri"] == INPUTS
-    assert training_config_two["image_uri"] == estimator_two.train_image()
+    assert training_config_two["image_uri"] == estimator_two.training_image_uri()
     assert training_config_two["metric_definitions"] is None
     assert training_config_two["static_hyperparameters"]["mini_batch_size"] == "4000"
     _assert_parameter_ranges(