Merge branch 'master' into transformer-kms

Author: nadiaya

CHANGELOG.rst

@@ -5,8 +5,9 @@ CHANGELOG
 1.9.3dev
 ========
 
-* bug-fix: Local Mode: Create output/data directory expected by SageMaker Container.
 * enhancement: Add support for volume KMS key to Transformer
+* bug-fix: Local Mode: Create output/data directory expected by SageMaker Container.
+* bug-fix: Estimator accepts the vpc configs made capable by 1.9.1
 
 1.9.2
 =====

src/sagemaker/chainer/README.rst

@@ -639,7 +639,7 @@ The Chainer Docker images have the following dependencies installed:
 +-----------------------------+-------------+
 | chainermn                   | 1.2.0       |
 +-----------------------------+-------------+
-| CUDA                        | 9.0         |
+| CUDA (GPU image only)       | 9.0         |
 +-----------------------------+-------------+
 | cupy                        | 4.0.0       |
 +-----------------------------+-------------+

src/sagemaker/estimator.py

@@ -431,7 +431,7 @@ class Estimator(EstimatorBase):
     def __init__(self, image_name, role, train_instance_count, train_instance_type,
                  train_volume_size=30, train_max_run=24 * 60 * 60, input_mode='File',
                  output_path=None, output_kms_key=None, base_job_name=None, sagemaker_session=None,
-                 hyperparameters=None):
+                 hyperparameters=None, tags=None, subnets=None, security_group_ids=None):
         """Initialize an ``Estimator`` instance.
 
         Args:
@@ -469,7 +469,8 @@ def __init__(self, image_name, role, train_instance_count, train_instance_type,
         self.hyperparam_dict = hyperparameters.copy() if hyperparameters else {}
         super(Estimator, self).__init__(role, train_instance_count, train_instance_type,
                                         train_volume_size, train_max_run, input_mode,
-                                        output_path, output_kms_key, base_job_name, sagemaker_session)
+                                        output_path, output_kms_key, base_job_name, sagemaker_session,
+                                        tags, subnets, security_group_ids)
 
     def train_image(self):
         """

src/sagemaker/mxnet/README.rst

@@ -590,7 +590,7 @@ The Docker images have the following dependencies installed:
 +-------------------------+--------------+-------------+-------------+-------------+
 | Python                  |   2.7 or 3.5 |   2.7 or 3.5|   2.7 or 3.5|   2.7 or 3.5|
 +-------------------------+--------------+-------------+-------------+-------------+
-| CUDA                    |          9.0 |         9.0 |         9.0 |         9.0 |
+| CUDA (GPU image only)   |          9.0 |         9.0 |         9.0 |         9.0 |
 +-------------------------+--------------+-------------+-------------+-------------+
 | numpy                   |       1.13.3 |      1.13.3 |      1.13.3 |      1.14.5 |
 +-------------------------+--------------+-------------+-------------+-------------+

src/sagemaker/pytorch/README.rst

@@ -667,6 +667,8 @@ The PyTorch Docker images have the following dependencies installed:
 +-----------------------------+---------------+
 | botocore                    | 1.10.35       |
 +-----------------------------+---------------+
+| CUDA (GPU image only)       | 9.0           |
++-----------------------------+---------------+
 | numpy                       | 1.14.3        |
 +-----------------------------+---------------+
 | Pillow                      | 5.1.0         |

src/sagemaker/session.py

@@ -119,8 +119,8 @@ def upload_data(self, path, bucket=None, key_prefix='data'):
         Args:
             path (str): Path (absolute or relative) of local file or directory to upload.
             bucket (str): Name of the S3 Bucket to upload to (default: None). If not specified, the
-                default bucket of the ``Session`` is used. If the bucket does not exist, the ``Session``
-                creates the bucket.
+                default bucket of the ``Session`` is used (if default bucket does not exist, the ``Session``
+                creates it).
             key_prefix (str): Optional S3 object key name prefix (default: 'data'). S3 uses the prefix to
                 create a directory structure for the bucket content that it display in the S3 console.
 

src/sagemaker/tensorflow/README.rst

@@ -850,6 +850,7 @@ The TensorFlow Docker images support Python 2.7. They include the following Pyth
 
 - boto3
 - botocore
+- CUDA 9.0 (GPU image only)
 - grpcio
 - numpy
 - pandas