update with aws:master

update with aws:master

Author: chuyang-deng

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,13 +2,17 @@
 
 *Description of changes:*
 
+*Testing done:*
+
 ## Merge Checklist
 
 _Put an `x` in the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request._
 
 - [ ] I have read the [CONTRIBUTING](https://github.com/aws/sagemaker-python-sdk/blob/master/CONTRIBUTING.md) doc
 - [ ] I used the commit message format described in [CONTRIBUTING](https://github.com/aws/sagemaker-python-sdk/blob/master/CONTRIBUTING.md#committing-your-change)
+- [ ] I have used the regional endpoint when creating S3 and/or STS clients (if appropriate)
 - [ ] I have added tests that prove my fix is effective or that my feature works (if appropriate)
-- [ ] I have updated any necessary [documentation](https://github.com/aws/sagemaker-python-sdk/blob/master/README.rst) (if appropriate)
+- [ ] I have checked that my tests are not configured for a specific region or account (if appropriate)
+- [ ] I have updated any necessary documentation, including [READMEs](https://github.com/aws/sagemaker-python-sdk/blob/master/README.rst) and [API docs](https://github.com/aws/sagemaker-python-sdk/tree/master/doc) (if appropriate)
 
 By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

CHANGELOG.md

@@ -1,5 +1,28 @@
 # Changelog
 
+## v1.46.0 (2019-12-12)
+
+### Features
+
+ * support Multi-Model endpoints
+
+### Bug fixes and other changes
+
+ * update PR template with items about tests, regional endpoints, and API docs
+
+## v1.45.2 (2019-12-10)
+
+### Bug fixes and other changes
+
+ * modify schedule cleanup to abide by latest validations
+ * lower log level when getting execution role from a SageMaker Notebook
+ * Fix "ValueError: too many values to unpack (expected 2)" is occurred in windows local mode
+ * allow ModelMonitor and Processor to take IAM role names (in addition to ARNs)
+
+### Documentation changes
+
+ * mention that the entry_point needs to be named inference.py for tfs
+
 ## v1.45.1 (2019-12-06)
 
 ### Bug fixes and other changes

VERSION

@@ -1 +1 @@
-1.45.2.dev0
+1.46.1.dev0

buildspec.yml

@@ -1,6 +1,10 @@
 version: 0.2
 
 phases:
+  pre_build:
+    commands:
+      - start-dockerd
+
   build:
     commands:
       - IGNORE_COVERAGE=-

doc/using_tf.rst

@@ -723,7 +723,8 @@ JSON options instead.
 Create Python Scripts for Custom Input and Output Formats
 ---------------------------------------------------------
 
-You can add your customized Python code to process your input and output data:
+You can add your customized Python code to process your input and output data.
+This customized Python code must be named ``inference.py`` and specified through the ``entry_point`` parameter:
 
 .. code::
 
@@ -736,8 +737,9 @@ You can add your customized Python code to process your input and output data:
 How to implement the pre- and/or post-processing handler(s)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Your entry point file should implement either a pair of ``input_handler``
-   and ``output_handler`` functions or a single ``handler`` function.
+Your entry point file must be named ``inference.py`` and should implement
+   either a pair of ``input_handler`` and ``output_handler`` functions or
+   a single ``handler`` function.
    Note that if ``handler`` function is implemented, ``input_handler``
    and ``output_handler`` are ignored.
 
@@ -905,6 +907,7 @@ processing. There are 2 ways to do this:
                   model_data='s3://mybucket/model.tar.gz',
                   role='MySageMakerRole')
 
+For more information, see: https://github.com/aws/sagemaker-tensorflow-serving-container#prepost-processing
 
 *************************************
 sagemaker.tensorflow.TensorFlow Class

src/sagemaker/automl/automl.py

@@ -10,7 +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.
-"""A class for SageMaker AutoML Job."""
+"""A class for SageMaker AutoML Jobs."""
 from __future__ import absolute_import
 
 from six import string_types

src/sagemaker/local/image.py

@@ -43,7 +43,6 @@
 DOCKER_COMPOSE_HTTP_TIMEOUT_ENV = "COMPOSE_HTTP_TIMEOUT"
 DOCKER_COMPOSE_HTTP_TIMEOUT = "120"
 
-
 # Environment variables to be set during training
 REGION_ENV_NAME = "AWS_REGION"
 TRAINING_JOB_NAME_ENV_NAME = "TRAINING_JOB_NAME"
@@ -256,7 +255,11 @@ def retrieve_artifacts(self, compose_data, output_data_config, job_name):
         for host in self.hosts:
             volumes = compose_data["services"][str(host)]["volumes"]
             for volume in volumes:
-                host_dir, container_dir = volume.split(":")
+                if re.search(r"^[A-Za-z]:", volume):
+                    unit, host_dir, container_dir = volume.split(":")
+                    host_dir = unit + ":" + host_dir
+                else:
+                    host_dir, container_dir = volume.split(":")
                 if container_dir == "/opt/ml/model":
                     sagemaker.local.utils.recursive_copy(host_dir, model_artifacts)
                 elif container_dir == "/opt/ml/output":
@@ -639,9 +642,7 @@ def __init__(self, host_dir, container_dir=None, channel=None):
         if container_dir and channel:
             raise ValueError("container_dir and channel cannot be declared together.")
 
-        self.container_dir = (
-            container_dir if container_dir else os.path.join("/opt/ml/input/data", channel)
-        )
+        self.container_dir = container_dir if container_dir else "/opt/ml/input/data/" + channel
         self.host_dir = host_dir
         if platform.system() == "Darwin" and host_dir.startswith("/var"):
             self.host_dir = os.path.join("/private", host_dir)

src/sagemaker/model_monitor/model_monitoring.py

@@ -22,24 +22,17 @@
 import logging
 import uuid
 
-from six.moves.urllib.parse import urlparse
 from six import string_types
-
+from six.moves.urllib.parse import urlparse
 from botocore.exceptions import ClientError
 
+from sagemaker.exceptions import UnexpectedStatusException
+from sagemaker.model_monitor.monitoring_files import Constraints, ConstraintViolations, Statistics
 from sagemaker.network import NetworkConfig
+from sagemaker.processing import Processor, ProcessingInput, ProcessingJob, ProcessingOutput
 from sagemaker.s3 import S3Uploader
-
-from sagemaker.utils import name_from_base
 from sagemaker.session import Session
-from sagemaker.processing import Processor
-from sagemaker.processing import ProcessingJob
-from sagemaker.processing import ProcessingInput
-from sagemaker.processing import ProcessingOutput
-from sagemaker.model_monitor.monitoring_files import Constraints, ConstraintViolations
-from sagemaker.model_monitor.monitoring_files import Statistics
-from sagemaker.exceptions import UnexpectedStatusException
-from sagemaker.utils import retries
+from sagemaker.utils import name_from_base, retries
 
 _DEFAULT_MONITOR_IMAGE_URI_WITH_PLACEHOLDERS = (
     "{}.dkr.ecr.{}.amazonaws.com/sagemaker-model-monitor-analyzer"
@@ -331,7 +324,7 @@ def create_monitoring_schedule(
             max_runtime_in_seconds=self.max_runtime_in_seconds,
             environment=self.env,
             network_config=network_config_dict,
-            role_arn=self.role,
+            role_arn=self.sagemaker_session.expand_role(self.role),
             tags=self.tags,
         )
 
@@ -390,7 +383,7 @@ def update_monitoring_schedule(
             network_config (sagemaker.network.NetworkConfig): A NetworkConfig
                 object that configures network isolation, encryption of
                 inter-container traffic, security group IDs, and subnets.
-            role (str): An AWS IAM role. The Amazon SageMaker jobs use this role.
+            role (str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.
             image_uri (str): The uri of the image to use for the jobs started by
                 the Monitor.
 
@@ -478,7 +471,7 @@ def update_monitoring_schedule(
             max_runtime_in_seconds=max_runtime_in_seconds,
             environment=env,
             network_config=network_config_dict,
-            role_arn=role,
+            role_arn=self.sagemaker_session.expand_role(self.role),
         )
 
         self._wait_for_schedule_changes_to_apply()
@@ -988,7 +981,7 @@ def __init__(
         creating Amazon SageMaker Monitoring Schedules to monitor SageMaker endpoints.
 
         Args:
-            role (str): An AWS IAM role. The Amazon SageMaker jobs use this role.
+            role (str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.
             instance_count (int): The number of instances to run the jobs with.
             instance_type (str): Type of EC2 instance to use for the job, for example,
                 'ml.m5.xlarge'.
@@ -1292,7 +1285,7 @@ def create_monitoring_schedule(
             max_runtime_in_seconds=self.max_runtime_in_seconds,
             environment=normalized_env,
             network_config=network_config_dict,
-            role_arn=self.role,
+            role_arn=self.sagemaker_session.expand_role(self.role),
             tags=self.tags,
         )
 
@@ -1355,7 +1348,7 @@ def update_monitoring_schedule(
                 inter-container traffic, security group IDs, and subnets.
             enable_cloudwatch_metrics (bool): Whether to publish cloudwatch metrics as part of
                 the baselining or monitoring jobs.
-            role (str): An AWS IAM role. The Amazon SageMaker jobs use this role.
+            role (str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.
 
         """
         monitoring_inputs = None
@@ -1449,7 +1442,7 @@ def update_monitoring_schedule(
             max_runtime_in_seconds=max_runtime_in_seconds,
             environment=normalized_env,
             network_config=network_config_dict,
-            role_arn=role,
+            role_arn=self.sagemaker_session.expand_role(self.role),
         )
 
         self._wait_for_schedule_changes_to_apply()