Merge branch 'master' into job-tags

Author: laurenyu

CHANGELOG.rst

@@ -5,7 +5,9 @@ CHANGELOG
 1.3.dev1
 ========
 
+* bug-fix: Remove __all__ and add noqa in __init__
 * bug-fix: Estimators: Change max_iterations hyperparameter key for KMeans
+* bug-fix: Estimators: Remove unused argument job_details for ``EstimatorBase.attach()``
 * feature: Estimators: add support for tagging training jobs
 
 1.3.0

src/sagemaker/__init__.py

@@ -12,31 +12,23 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
-from sagemaker import estimator
-from sagemaker.amazon.kmeans import KMeans, KMeansModel, KMeansPredictor
-from sagemaker.amazon.pca import PCA, PCAModel, PCAPredictor
-from sagemaker.amazon.lda import LDA, LDAModel, LDAPredictor
-from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerModel, LinearLearnerPredictor
-from sagemaker.amazon.factorization_machines import FactorizationMachines, FactorizationMachinesModel
-from sagemaker.amazon.factorization_machines import FactorizationMachinesPredictor
-from sagemaker.amazon.ntm import NTM, NTMModel, NTMPredictor
-from sagemaker.amazon.randomcutforest import RandomCutForest, RandomCutForestModel, RandomCutForestPredictor
+from sagemaker import estimator  # noqa: F401
+from sagemaker.amazon.kmeans import KMeans, KMeansModel, KMeansPredictor  # noqa: F401
+from sagemaker.amazon.pca import PCA, PCAModel, PCAPredictor  # noqa: F401
+from sagemaker.amazon.lda import LDA, LDAModel, LDAPredictor  # noqa: F401
+from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerModel, LinearLearnerPredictor  # noqa: F401
+from sagemaker.amazon.factorization_machines import FactorizationMachines, FactorizationMachinesModel  # noqa: F401
+from sagemaker.amazon.factorization_machines import FactorizationMachinesPredictor  # noqa: F401
+from sagemaker.amazon.ntm import NTM, NTMModel, NTMPredictor  # noqa: F401
+from sagemaker.amazon.randomcutforest import (RandomCutForest, RandomCutForestModel,  # noqa: F401
+                                              RandomCutForestPredictor)
 
-from sagemaker.local.local_session import LocalSession
+from sagemaker.local.local_session import LocalSession  # noqa: F401
 
-from sagemaker.model import Model
-from sagemaker.predictor import RealTimePredictor
-from sagemaker.session import Session
-from sagemaker.session import container_def
-from sagemaker.session import production_variant
-from sagemaker.session import s3_input
-from sagemaker.session import get_execution_role
-
-
-__all__ = ['estimator', 'KMeans', 'KMeansModel', 'KMeansPredictor', 'PCA', 'PCAModel', 'PCAPredictor', 'LinearLearner',
-           'LinearLearnerModel', 'LinearLearnerPredictor',
-           'LDA', 'LDAModel', 'LDAPredictor',
-           'FactorizationMachines', 'FactorizationMachinesModel', 'FactorizationMachinesPredictor',
-           'RandomCutForest', 'RandomCutForestModel', 'RandomCutForestPredictor',
-           'Model', 'NTM', 'NTMModel', 'NTMPredictor', 'RealTimePredictor', 'Session', 'LocalSession',
-           'container_def', 's3_input', 'production_variant', 'get_execution_role']
+from sagemaker.model import Model  # noqa: F401
+from sagemaker.predictor import RealTimePredictor  # noqa: F401
+from sagemaker.session import Session  # noqa: F401
+from sagemaker.session import container_def  # noqa: F401
+from sagemaker.session import production_variant  # noqa: F401
+from sagemaker.session import s3_input  # noqa: F401
+from sagemaker.session import get_execution_role  # noqa: F401

src/sagemaker/estimator.py

@@ -186,7 +186,7 @@ def _from_training_job(cls, init_params, hyperparameters, image, sagemaker_sessi
         raise NotImplementedError()
 
     @classmethod
-    def attach(cls, training_job_name, sagemaker_session=None, job_details=None):
+    def attach(cls, training_job_name, sagemaker_session=None):
         """Attach to an existing training job.
 
         Create an Estimator bound to an existing training job, each subclass is responsible to implement

src/sagemaker/mxnet/__init__.py

@@ -10,9 +10,7 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-from __future__ import absolute_import
+from __future__ import absolute_import  # noqa: F401
 
-from sagemaker.mxnet.estimator import MXNet
-from sagemaker.mxnet.model import MXNetModel, MXNetPredictor
-
-__all__ = ['MXNet', 'MXNetModel', 'MXNetPredictor']
+from sagemaker.mxnet.estimator import MXNet  # noqa: F401
+from sagemaker.mxnet.model import MXNetModel, MXNetPredictor  # noqa: F401

src/sagemaker/tensorflow/__init__.py

@@ -20,14 +20,12 @@
 # classes for tensorflow serving. Currently tensorflow_serving_api can only be pip-installed for python 2.
 sys.path.append(os.path.dirname(__file__))
 
-from distutils.version import LooseVersion  # noqa: E402
-import tensorflow  # noqa: E402
+from distutils.version import LooseVersion  # noqa: E402, F401
+import tensorflow  # noqa: E402, F401
 
 if LooseVersion(tensorflow.__version__) < LooseVersion("1.3.0"):
     message = 'Tensorflow version must be >= 1.3.0. Current version: {}'.format(tensorflow.__version__)
     raise AssertionError(message)
 
-from sagemaker.tensorflow.estimator import TensorFlow  # noqa: E402
-from sagemaker.tensorflow.model import TensorFlowModel, TensorFlowPredictor  # noqa: E402
-
-__all__ = ['TensorFlow', 'TensorFlowModel', 'TensorFlowPredictor']
+from sagemaker.tensorflow.estimator import TensorFlow  # noqa: E402, F401
+from sagemaker.tensorflow.model import TensorFlowModel, TensorFlowPredictor  # noqa: E402, F401