Merge branch 'master' into master

Author: ajaykarpur

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## v2.17.0 (2020-12-02)
+
+### Features
+
+ * bump minor version for re:Invent 2020 features
+
 ## v2.16.4 (2020-12-01)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.16.5.dev0
+2.17.1.dev0

doc/frameworks/pytorch/using_pytorch.rst

@@ -132,7 +132,7 @@ Using third-party libraries
 ---------------------------
 
 When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvision``, and ``numpy``.
-For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch#sagemaker-pytorch-docker-containers>`_.
+For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <https://github.com/aws/deep-learning-containers/tree/master/pytorch>`_.
 
 If there are other packages you want to use with your script, you can include a ``requirements.txt`` file in the same directory as your training script to install other dependencies at runtime. Both ``requirements.txt`` and your training script should be put in the same folder. You must specify this folder in ``source_dir`` argument when creating PyTorch estimator.
 

src/sagemaker/lineage/artifact.py

@@ -81,6 +81,9 @@ class Artifact(_base_types.Record):
     def save(self):
         """Save the state of this Artifact to SageMaker.
 
+        Note that this method must be run from a SageMaker context such as Studio or a training job
+        due to restrictions on the CreateArtifact API.
+
         Returns:
             Artifact: A SageMaker `Artifact` object.
         """

tests/data/tensorflow_mnist/mnist.py

@@ -159,7 +159,7 @@ def serving_input_fn():
 
     # Train the model
     train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
-        x={"x": train_data}, y=train_labels, batch_size=50, num_epochs=None, shuffle=True
+        x={"x": train_data}, y=train_labels, batch_size=50, num_epochs=None, shuffle=False
     )
 
     # Evaluate the model and print results

tests/integ/test_transformer.py

@@ -420,7 +420,7 @@ def test_transform_tf_kms_network_isolation(
         with open(os.path.join(tmpdir, "tf-batch-output", "data.csv.out")) as f:
             result = json.load(f)
             assert len(result["predictions"][0]["probabilities"]) == 10
-            assert result["predictions"][0]["classes"] == 1
+            assert result["predictions"][0]["classes"] >= 1
 
 
 def _create_transformer_and_transform_job(