Merge branch 'zwei' into clean-up-tests-bc-no-more-python-2-skl

Author: laurenyu

doc/api/inference/predictors.rst

@@ -3,7 +3,7 @@ Predictors
 
 Make real-time predictions against SageMaker endpoints with Python objects
 
-.. autoclass:: sagemaker.predictor.RealTimePredictor
+.. autoclass:: sagemaker.predictor.Predictor
     :members:
     :undoc-members:
     :show-inheritance:

doc/frameworks/chainer/using_chainer.rst

@@ -325,7 +325,7 @@ You can provide your own implementations for these functions in your hosting scr
 If you omit any definition then the SageMaker Chainer model server will use its default implementation for that
 function.
 
-The ``RealTimePredictor`` used by Chainer in the SageMaker Python SDK serializes NumPy arrays to the `NPY <https://docs.scipy.org/doc/numpy/neps/npy-format.html>`_ format
+The ``Predictor`` used by Chainer in the SageMaker Python SDK serializes NumPy arrays to the `NPY <https://docs.scipy.org/doc/numpy/neps/npy-format.html>`_ format
 by default, with Content-Type ``application/x-npy``. The SageMaker Chainer model server can deserialize NPY-formatted
 data (along with JSON and CSV data).
 

doc/frameworks/pytorch/using_pytorch.rst

@@ -417,7 +417,7 @@ You can provide your own implementations for these functions in your hosting scr
 If you omit any definition then the SageMaker PyTorch model server will use its default implementation for that
 function.
 
-The ``RealTimePredictor`` used by PyTorch in the SageMaker Python SDK serializes NumPy arrays to the `NPY <https://docs.scipy.org/doc/numpy/neps/npy-format.html>`_ format
+The ``Predictor`` used by PyTorch in the SageMaker Python SDK serializes NumPy arrays to the `NPY <https://docs.scipy.org/doc/numpy/neps/npy-format.html>`_ format
 by default, with Content-Type ``application/x-npy``. The SageMaker PyTorch model server can deserialize NPY-formatted
 data (along with JSON and CSV data).
 

doc/frameworks/sklearn/using_sklearn.rst

@@ -299,7 +299,7 @@ You can provide your own implementations for these functions in your hosting scr
 If you omit any definition then the SageMaker Scikit-learn model server will use its default implementation for that
 function.
 
-The ``RealTimePredictor`` used by Scikit-learn in the SageMaker Python SDK serializes NumPy arrays to the `NPY <https://docs.scipy.org/doc/numpy/neps/npy-format.html>`_ format
+The ``Predictor`` used by Scikit-learn in the SageMaker Python SDK serializes NumPy arrays to the `NPY <https://docs.scipy.org/doc/numpy/neps/npy-format.html>`_ format
 by default, with Content-Type ``application/x-npy``. The SageMaker Scikit-learn model server can deserialize NPY-formatted
 data (along with JSON and CSV data).
 

doc/frameworks/tensorflow/upgrade_from_legacy.rst

@@ -206,16 +206,16 @@ From a model:
 Predictors
 ----------
 
-If you want to use your model for endpoints, then you can use the :class:`sagemaker.predictor.RealTimePredictor` class instead of the legacy ``sagemaker.tensorflow.TensorFlowPredictor`` class:
+If you want to use your model for endpoints, then you can use the :class:`sagemaker.predictor.Predictor` class instead of the legacy ``sagemaker.tensorflow.TensorFlowPredictor`` class:
 
 .. code:: python
 
     from sagemaker.model import FrameworkModel
-    from sagemaker.predictor import RealTimePredictor
+    from sagemaker.predictor import Predictor
 
     model = FrameworkModel(
         ...
-        predictor_cls=RealTimePredictor,
+        predictor_cls=Predictor,
     )
 
     predictor = model.deploy(...)

doc/overview.rst

@@ -1124,7 +1124,7 @@ How do I make predictions against an existing endpoint?
 Create a ``Predictor`` object and provide it with your endpoint name,
 then call its ``predict()`` method with your input.
 
-You can use either the generic ``RealTimePredictor`` class, which by default does not perform any serialization/deserialization transformations on your input,
+You can use either the generic ``Predictor`` class, which by default does not perform any serialization/deserialization transformations on your input,
 but can be configured to do so through constructor arguments:
 http://sagemaker.readthedocs.io/en/stable/predictors.html
 

src/sagemaker/__init__.py

@@ -49,7 +49,7 @@
 
 from sagemaker.model import Model, ModelPackage  # noqa: F401
 from sagemaker.pipeline import PipelineModel  # noqa: F401
-from sagemaker.predictor import RealTimePredictor  # noqa: F401
+from sagemaker.predictor import Predictor  # noqa: F401
 from sagemaker.processing import Processor, ScriptProcessor  # noqa: F401
 from sagemaker.session import Session  # noqa: F401
 from sagemaker.session import container_def, pipeline_container_def  # noqa: F401

src/sagemaker/algorithm.py

@@ -18,7 +18,7 @@
 from sagemaker import vpc_utils
 from sagemaker.estimator import EstimatorBase
 from sagemaker.transformer import Transformer
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 
 
 class AlgorithmEstimator(EstimatorBase):
@@ -246,15 +246,15 @@ def create_model(
         """Create a model to deploy.
 
         The serializer, deserializer, content_type, and accept arguments are
-        only used to define a default RealTimePredictor. They are ignored if an
+        only used to define a default Predictor. They are ignored if an
         explicit predictor class is passed in. Other arguments are passed
         through to the Model class.
 
         Args:
             role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
                 which is also used during transform jobs. If not specified, the
                 role from the Estimator will be used.
-            predictor_cls (RealTimePredictor): The predictor class to use when
+            predictor_cls (Predictor): The predictor class to use when
                 deploying the model.
             serializer (callable): Should accept a single argument, the input
                 data, and return a sequence of bytes. May provide a content_type
@@ -285,9 +285,7 @@ def create_model(
         if predictor_cls is None:
 
             def predict_wrapper(endpoint, session):
-                return RealTimePredictor(
-                    endpoint, session, serializer, deserializer, content_type, accept
-                )
+                return Predictor(endpoint, session, serializer, deserializer, content_type, accept)
 
             predictor_cls = predict_wrapper
 

src/sagemaker/amazon/factorization_machines.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt, isin, ge
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -261,13 +261,13 @@ def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT, **kwargs):
         )
 
 
-class FactorizationMachinesPredictor(RealTimePredictor):
+class FactorizationMachinesPredictor(Predictor):
     """Performs binary-classification or regression prediction from input
     vectors.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/amazon/ipinsights.py

@@ -16,7 +16,7 @@
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, le
-from sagemaker.predictor import RealTimePredictor, csv_serializer, json_deserializer
+from sagemaker.predictor import Predictor, csv_serializer, json_deserializer
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -173,13 +173,13 @@ def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
         )
 
 
-class IPInsightsPredictor(RealTimePredictor):
+class IPInsightsPredictor(Predictor):
     """Returns dot product of entity and IP address embeddings as a score for
     compatibility.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain two columns. The first column should contain the entity ID. The
     second column should contain the IPv4 address in dot notation.
     """

src/sagemaker/amazon/kmeans.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt, isin, ge, le
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -194,12 +194,12 @@ def hyperparameters(self):
         return hp_dict
 
 
-class KMeansPredictor(RealTimePredictor):
+class KMeansPredictor(Predictor):
     """Assigns input vectors to their closest cluster in a KMeans model.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/amazon/knn.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, isin
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -182,12 +182,12 @@ def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
         )
 
 
-class KNNPredictor(RealTimePredictor):
+class KNNPredictor(Predictor):
     """Performs classification or regression prediction from input vectors.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/amazon/lda.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -166,12 +166,12 @@ def _prepare_for_training(  # pylint: disable=signature-differs
         )
 
 
-class LDAPredictor(RealTimePredictor):
+class LDAPredictor(Predictor):
     """Transforms input vectors to lower-dimesional representations.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/amazon/linear_learner.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import isin, gt, lt, ge, le
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -426,13 +426,13 @@ def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
         )
 
 
-class LinearLearnerPredictor(RealTimePredictor):
+class LinearLearnerPredictor(Predictor):
     """Performs binary-classification or regression prediction from input
     vectors.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/amazon/ntm.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, le, isin
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -196,12 +196,12 @@ def _prepare_for_training(  # pylint: disable=signature-differs
         )
 
 
-class NTMPredictor(RealTimePredictor):
+class NTMPredictor(Predictor):
     """Transforms input vectors to lower-dimesional representations.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/amazon/object2vec.py

@@ -16,7 +16,7 @@
 from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, le, isin
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -184,7 +184,7 @@ def __init__(
         may be deployed to an Amazon SageMaker Endpoint by invoking
         :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as
         deploying an Endpoint, deploy returns a
-        :class:`~sagemaker.amazon.RealTimePredictor` object that can be used for
+        :class:`~sagemaker.amazon.Predictor` object that can be used for
         inference calls using the trained model hosted in the SageMaker
         Endpoint.
 
@@ -358,7 +358,7 @@ def __init__(self, model_data, role, sagemaker_session=None, **kwargs):
             image,
             model_data,
             role,
-            predictor_cls=RealTimePredictor,
+            predictor_cls=Predictor,
             sagemaker_session=sagemaker_session,
             **kwargs
         )

src/sagemaker/amazon/pca.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import gt, isin
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -178,12 +178,12 @@ def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
         )
 
 
-class PCAPredictor(RealTimePredictor):
+class PCAPredictor(Predictor):
     """Transforms input vectors to lower-dimesional representations.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/amazon/randomcutforest.py

@@ -17,7 +17,7 @@
 from sagemaker.amazon.common import numpy_to_record_serializer, record_deserializer
 from sagemaker.amazon.hyperparameter import Hyperparameter as hp  # noqa
 from sagemaker.amazon.validation import ge, le
-from sagemaker.predictor import RealTimePredictor
+from sagemaker.predictor import Predictor
 from sagemaker.model import Model
 from sagemaker.session import Session
 from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
@@ -157,12 +157,12 @@ def _prepare_for_training(self, records, mini_batch_size=None, job_name=None):
         )
 
 
-class RandomCutForestPredictor(RealTimePredictor):
+class RandomCutForestPredictor(Predictor):
     """Assigns an anomaly score to each of the datapoints provided.
 
     The implementation of
-    :meth:`~sagemaker.predictor.RealTimePredictor.predict` in this
-    `RealTimePredictor` requires a numpy ``ndarray`` as input. The array should
+    :meth:`~sagemaker.predictor.Predictor.predict` in this
+    `Predictor` requires a numpy ``ndarray`` as input. The array should
     contain the same number of columns as the feature-dimension of the data used
     to fit the model this Predictor performs inference on.
 

src/sagemaker/chainer/model.py

@@ -24,13 +24,13 @@
 )
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.chainer import defaults
-from sagemaker.predictor import RealTimePredictor, npy_serializer, numpy_deserializer
+from sagemaker.predictor import Predictor, npy_serializer, numpy_deserializer
 
 logger = logging.getLogger("sagemaker")
 
 
-class ChainerPredictor(RealTimePredictor):
-    """A RealTimePredictor for inference against Chainer Endpoints.
+class ChainerPredictor(Predictor):
+    """A Predictor for inference against Chainer Endpoints.
 
     This is able to serialize Python lists, dictionaries, and numpy arrays to
     multidimensional tensors for Chainer inference.

src/sagemaker/cli/compatibility/v2/ast_transformer.py

@@ -23,6 +23,7 @@
     modifiers.tf_legacy_mode.TensorBoardParameterRemover(),
     modifiers.deprecated_params.TensorFlowScriptModeParameterRemover(),
     modifiers.tfs.TensorFlowServingConstructorRenamer(),
+    modifiers.airflow.ModelConfigArgModifier(),
 ]
 
 IMPORT_MODIFIERS = [modifiers.tfs.TensorFlowServingImportRenamer()]

src/sagemaker/cli/compatibility/v2/modifiers/__init__.py

@@ -14,6 +14,7 @@
 from __future__ import absolute_import
 
 from sagemaker.cli.compatibility.v2.modifiers import (  # noqa: F401 (imported but unused)
+    airflow,
     deprecated_params,
     framework_version,
     tf_legacy_mode,