feature: airflow workflow now prepares estimators for training

setup.py

@@ -86,6 +86,7 @@ def read_version():
             "pandas",
             "black==19.3b0 ; python_version >= '3.6'",
             "stopit==1.1.2",
+            "apache-airflow==1.10.5",
         ]
     },
     entry_points={"console_scripts": ["sagemaker=sagemaker.cli.main:main"]},

src/sagemaker/amazon/amazon_estimator.py

@@ -127,6 +127,21 @@ class constructor
         del init_params["image"]
         return init_params
 
+    def prepare_workflow_for_training(self, records=None, mini_batch_size=None, job_name=None):
+        """Calls _prepare_for_training. Used when setting up a workflow.
+
+        Args:
+            records (:class:`~RecordSet`): The records to train this ``Estimator`` on.
+            mini_batch_size (int or None): The size of each mini-batch to use when
+                training. If ``None``, a default value will be used.
+            job_name (str): Name of the training job to be created. If not
+                specified, one is generated, using the base name given to the
+                constructor if applicable.
+        """
+        self._prepare_for_training(
+            records=records, mini_batch_size=mini_batch_size, job_name=job_name
+        )
+
     def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
         """Set hyperparameters needed for training.
 

src/sagemaker/estimator.py

@@ -261,6 +261,16 @@ def enable_network_isolation(self):
         """
         return False
 
+    def prepare_workflow_for_training(self, job_name=None):
+        """Calls _prepare_for_training. Used when setting up a workflow.
+
+        Args:
+            job_name (str): Name of the training job to be created. If not
+                specified, one is generated, using the base name given to the
+                constructor if applicable.
+        """
+        self._prepare_for_training(job_name=job_name)
+
     def _prepare_for_training(self, job_name=None):
         """Set any values in the estimator that need to be set before training.
 

src/sagemaker/tensorflow/estimator.py

@@ -27,6 +27,7 @@
 from sagemaker.tensorflow.defaults import TF_VERSION
 from sagemaker.tensorflow.model import TensorFlowModel
 from sagemaker.tensorflow.serving import Model
+from sagemaker.transformer import Transformer
 from sagemaker import utils
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
 
@@ -755,8 +756,31 @@ def transformer(
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
         """
-
         role = role or self.role
+
+        if self.latest_training_job is None:
+            logging.warning(
+                "No finished training job found associated with this estimator. Please make sure "
+                "this estimator is only used for building workflow config"
+            )
+            return Transformer(
+                self._current_job_name,
+                instance_count,
+                instance_type,
+                strategy=strategy,
+                assemble_with=assemble_with,
+                output_path=output_path,
+                output_kms_key=output_kms_key,
+                accept=accept,
+                max_concurrent_transforms=max_concurrent_transforms,
+                max_payload=max_payload,
+                env=env or {},
+                tags=tags,
+                base_transform_job_name=self.base_job_name,
+                volume_kms_key=volume_kms_key,
+                sagemaker_session=self.sagemaker_session,
+            )
+
         model = self.create_model(
             model_server_workers=model_server_workers,
             role=role,

src/sagemaker/workflow/airflow.py

@@ -34,10 +34,14 @@ def prepare_framework(estimator, s3_operations):
     if estimator.code_location is not None:
         bucket, key = fw_utils.parse_s3_url(estimator.code_location)
         key = os.path.join(key, estimator._current_job_name, "source", "sourcedir.tar.gz")
+    elif estimator.uploaded_code is not None:
+        bucket, key = fw_utils.parse_s3_url(estimator.uploaded_code.s3_prefix)
     else:
         bucket = estimator.sagemaker_session._default_bucket
         key = os.path.join(estimator._current_job_name, "source", "sourcedir.tar.gz")
+
     script = os.path.basename(estimator.entry_point)
+
     if estimator.source_dir and estimator.source_dir.lower().startswith("s3://"):
         code_dir = estimator.source_dir
         estimator.uploaded_code = fw_utils.UploadedCode(s3_prefix=code_dir, script_name=script)
@@ -96,7 +100,7 @@ def prepare_amazon_algorithm_estimator(estimator, inputs, mini_batch_size=None):
     estimator.mini_batch_size = mini_batch_size
 
 
-def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=None):
+def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=None):  # noqa: C901
     """Export Airflow base training config from an estimator
 
     Args:
@@ -134,6 +138,13 @@ def training_base_config(estimator, inputs=None, job_name=None, mini_batch_size=
         dict: Training config that can be directly used by
         SageMakerTrainingOperator in Airflow.
     """
+    if isinstance(estimator, sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase):
+        estimator.prepare_workflow_for_training(
+            records=inputs, mini_batch_size=mini_batch_size, job_name=job_name
+        )
+    else:
+        estimator.prepare_workflow_for_training(job_name=job_name)
+
     default_bucket = estimator.sagemaker_session.default_bucket()
     s3_operations = {}
 
@@ -528,6 +539,7 @@ def model_config_from_estimator(
             model_server_workers=model_server_workers,
             role=role,
             vpc_config_override=vpc_config_override,
+            entry_point=estimator.entry_point,
         )
     else:
         raise TypeError(

src/sagemaker/xgboost/estimator.py

@@ -145,6 +145,12 @@ def create_model(
                 See :func:`~sagemaker.xgboost.model.XGBoostModel` for full details.
         """
         role = role or self.role
+
+        # Remove unwanted entry_point kwarg
+        if "entry_point" in kwargs:
+            logger.debug("Removing unused entry_point argument: %s", str(kwargs["entry_point"]))
+            kwargs = {k: v for k, v in kwargs.items() if k != "entry_point"}
+
         return XGBoostModel(
             self.model_data,
             role,