aws · laurenyu · Jul 17, 2018 · Jul 10, 2018 · Jul 12, 2018 · Jul 12, 2018
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -2,6 +2,12 @@
 CHANGELOG
 =========
 
+1.7.0
+=====
+
+* feature: Transformer: add support for batch transform jobs
+* feature: Documentation: add instructions for using Pipe Mode with TensorFlow
+
 1.6.1
 =====
 

diff --git a/README.rst b/README.rst
@@ -32,7 +32,8 @@ Table of Contents
 7. `AWS SageMaker Estimators <#aws-sagemaker-estimators>`__
 8. `BYO Docker Containers with SageMaker Estimators <#byo-docker-containers-with-sagemaker-estimators>`__
 9. `SageMaker Automatic Model Tuning <#sagemaker-automatic-model-tuning>`__
-10. `BYO Model <#byo-model>`__
+10. `SageMaker Batch Transform <#sagemaker-batch-transform>`__
+11. `BYO Model <#byo-model>`__
 
 
 Getting SageMaker Python SDK
@@ -50,7 +51,7 @@ You can install from source by cloning this repository and issuing a pip install
 
     git clone https://github.com/aws/sagemaker-python-sdk.git
     python setup.py sdist
-    pip install dist/sagemaker-1.6.1.tar.gz
+    pip install dist/sagemaker-1.7.0.tar.gz
 
 Supported Python versions
 ~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -375,6 +376,39 @@ For more detailed explanations of the classes that this library provides for aut
 - `API docs for analytics classes <https://sagemaker.readthedocs.io/en/latest/analytics.html>`__
 
 
+SageMaker Batch Transform
+-------------------------
+
+Once you have a trained model, you can use Amazon SageMaker Batch Transform to perform inferences with the model.
+Batch Transform manages all compute resources necessary, including launching instances to deploy endpoints and deleting them afterward.
+You can read more about SageMaker Batch Transform in the `AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-batch.html>`__.
+
+If you have trained the model using a SageMaker Python SDK Estimator, you can simply invoke ``transformer()`` to create a ``Transformer`` for the training job:
+
+.. code:: python
+
+    transformer = estimator.transformer(instance_count=1, instance_type='ml.m4.xlarge')
+
+Alternatively, if you already have a SageMaker Model, you can instantiate a ``Transformer`` directly with its constructor:
+
+.. code:: python
+
+    transformer = Transformer(model_name='my-previously-trained-model',
+                              instance_count=1,
+                              instance_type='ml.m4.xlarge')
+
+For a full list of the possible options to configure through either of these methods, please refer to the API docs for `Estimator <https://sagemaker.readthedocs.io/en/latest/estimators.html#sagemaker.estimator.Estimator.transformer>`__ or `Transformer <https://sagemaker.readthedocs.io/en/latest/transformer.html#sagemaker.transformer.Transformer>`__.
+
+Once you've created a ``Transformer`` object, you can invoke ``transform()`` to being a batch transform job with the S3 location of your data.
+You can also specify other attributes about your data, such as the content type.
+
+.. code:: python
+
+    transformer.transform('s3://my-bucket/batch-transform-input')
+
+For more details about what can be specified here, please refer to the `API docs <https://sagemaker.readthedocs.io/en/latest/transformer.html#sagemaker.transformer.Transformer.transform>`__.
+
+
 FAQ
 ---
 
@@ -422,7 +456,7 @@ Example code using the TensorFlow predictor:
 
 
 BYO Model
------------------------------------------------
+---------
 You can also create an endpoint from an existing model rather than training one - i.e. bring your own model.
 
 First, package the files for the trained model into a ``.tar.gz`` file, and upload the archive to S3.

diff --git a/doc/index.rst b/doc/index.rst
@@ -16,6 +16,7 @@ The SageMaker Python SDK consists of a few primary interfaces:
     estimators
     tuner
     predictors
+    transformer
     session
     model
     analytics

diff --git a/doc/transformer.rst b/doc/transformer.rst
@@ -0,0 +1,7 @@
+Transformer
+-----------
+
+.. autoclass:: sagemaker.transformer.Transformer
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/setup.py b/setup.py
@@ -23,7 +23,7 @@ def read(fname):
 
 
 setup(name="sagemaker",
-      version="1.6.1",
+      version="1.7.0",
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},

diff --git a/src/sagemaker/estimator.py b/src/sagemaker/estimator.py
@@ -30,7 +30,8 @@
 from sagemaker.predictor import RealTimePredictor
 from sagemaker.session import Session
 from sagemaker.session import s3_input
-from sagemaker.utils import base_name_from_image, name_from_base, get_config_value
+from sagemaker.transformer import Transformer
+from sagemaker.utils import base_name_from_image, name_from_base, name_from_image, get_config_value
 
 
 class EstimatorBase(with_metaclass(ABCMeta, object)):
@@ -253,8 +254,7 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, **kw
             sagemaker.predictor.RealTimePredictor: A predictor that provides a ``predict()`` method,
                 which can be used to send requests to the Amazon SageMaker endpoint and obtain inferences.
         """
-        if not self.latest_training_job:
-            raise RuntimeError('Estimator has not been fit yet.')
+        self._ensure_latest_training_job()
         endpoint_name = endpoint_name or self.latest_training_job.name
         self.deploy_instance_type = instance_type
         return self.create_model(**kwargs).deploy(
@@ -314,10 +314,43 @@ def delete_endpoint(self):
         Raises:
             ValueError: If the endpoint does not exist.
         """
-        if self.latest_training_job is None:
-            raise ValueError('Endpoint was not created yet')
+        self._ensure_latest_training_job(error_message='Endpoint was not created yet')
         self.sagemaker_session.delete_endpoint(self.latest_training_job.name)
 
+    def transformer(self, instance_count, instance_type, strategy=None, assemble_with=None, output_path=None,
+                    output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None,
+                    max_payload=None, tags=None):
+        """Return a ``Transformer`` that uses a SageMaker Model based on the training job. It reuses the
+        SageMaker Session and base job name used by the Estimator.
+
+        Args:
+            instance_count (int): Number of EC2 instances to use.
+            instance_type (str): Type of EC2 instance to use, for example, 'ml.c4.xlarge'.
+            strategy (str): The strategy used to decide how to batch records in a single request (default: None).
+                Valid values: 'MULTI_RECORD' and 'SINGLE_RECORD'.
+            assemble_with (str): How the output is assembled (default: None). Valid values: 'Line' or 'None'.
+            output_path (str): S3 location for saving the transform result. If not specified, results are stored to
+                a default bucket.
+            output_kms_key (str): Optional. KMS key ID for encrypting the transform output (default: None).
+            accept (str): The content type accepted by the endpoint deployed during the transform job.
+            env (dict): Environment variables to be set for use during the transform job (default: None).
+            max_concurrent_transforms (int): The maximum number of HTTP requests to be made to
+                each individual transform container at one time.
+            max_payload (int): Maximum size of the payload in a single HTTP request to the container in MB.
+            tags (list[dict]): List of tags for labeling a transform job. If none specified, then the tags used for
+                the training job are used for the transform job.
+        """
+        self._ensure_latest_training_job()
+
+        model_name = self.sagemaker_session.create_model_from_job(self.latest_training_job.name)
+        tags = tags or self.tags
+
+        return Transformer(model_name, instance_count, instance_type, strategy=strategy, assemble_with=assemble_with,
+                           output_path=output_path, output_kms_key=output_kms_key, accept=accept,
+                           max_concurrent_transforms=max_concurrent_transforms, max_payload=max_payload,
+                           env=env, tags=tags, base_transform_job_name=self.base_job_name,
+                           sagemaker_session=self.sagemaker_session)
+
     @property
     def training_job_analytics(self):
         """Return a ``TrainingJobAnalytics`` object for the current training job.
@@ -326,6 +359,10 @@ def training_job_analytics(self):
             raise ValueError('Estimator is not associated with a TrainingJob')
         return TrainingJobAnalytics(self._current_job_name, sagemaker_session=self.sagemaker_session)
 
+    def _ensure_latest_training_job(self, error_message='Estimator is not associated with a training job'):
+        if self.latest_training_job is None:
+            raise ValueError(error_message)
+
 
 class _TrainingJob(_Job):
     def __init__(self, sagemaker_session, training_job_name):
@@ -698,6 +735,50 @@ def _update_init_params(cls, hp, tf_arguments):
                 updated_params[argument] = value
         return updated_params
 
+    def transformer(self, instance_count, instance_type, strategy=None, assemble_with=None, output_path=None,
+                    output_kms_key=None, accept=None, env=None, max_concurrent_transforms=None,
+                    max_payload=None, tags=None, model_server_workers=None):
+        """Return a ``Transformer`` that uses a SageMaker Model based on the training job. It reuses the
+        SageMaker Session and base job name used by the Estimator.
+
+        Args:
+            instance_count (int): Number of EC2 instances to use.
+            instance_type (str): Type of EC2 instance to use, for example, 'ml.c4.xlarge'.
+            strategy (str): The strategy used to decide how to batch records in a single request (default: None).
+                Valid values: 'MULTI_RECORD' and 'SINGLE_RECORD'.
+            assemble_with (str): How the output is assembled (default: None). Valid values: 'Line' or 'None'.
+            output_path (str): S3 location for saving the transform result. If not specified, results are stored to
+                a default bucket.
+            output_kms_key (str): Optional. KMS key ID for encrypting the transform output (default: None).
+            accept (str): The content type accepted by the endpoint deployed during the transform job.
+            env (dict): Environment variables to be set for use during the transform job (default: None).
+            max_concurrent_transforms (int): The maximum number of HTTP requests to be made to
+                each individual transform container at one time.
+            max_payload (int): Maximum size of the payload in a single HTTP request to the container in MB.
+            tags (list[dict]): List of tags for labeling a transform job. If none specified, then the tags used for
+                the training job are used for the transform job.
+            model_server_workers (int): Optional. The number of worker processes used by the inference server.
+                If None, server will use one worker per vCPU.
+        """
+        self._ensure_latest_training_job()
+
+        model = self.create_model(model_server_workers=model_server_workers)
+
+        container_def = model.prepare_container_def(instance_type)
+        model_name = model.name or name_from_image(container_def['Image'])
+        self.sagemaker_session.create_model(model_name, self.role, container_def)
+
+        transform_env = model.env.copy()
+        if env is not None:
+            transform_env.update(env)
+
+        tags = tags or self.tags
+        return Transformer(model_name, instance_count, instance_type, strategy=strategy, assemble_with=assemble_with,
+                           output_path=output_path, output_kms_key=output_kms_key, accept=accept,
+                           max_concurrent_transforms=max_concurrent_transforms, max_payload=max_payload,
+                           env=transform_env, tags=tags, base_transform_job_name=self.base_job_name,
+                           sagemaker_session=self.sagemaker_session)
+
 
 def _s3_uri_prefix(channel_name, s3_data):
     if isinstance(s3_data, s3_input):

diff --git a/src/sagemaker/job.py b/src/sagemaker/job.py
@@ -104,17 +104,14 @@ def _format_string_uri_input(input):
             elif input.startswith('file://'):
                 return file_input(input)
             else:
-                raise ValueError(
-                    'Training input data must be a valid S3 or FILE URI: must start with "s3://" or '
-                    '"file://"')
+                raise ValueError('Training input data must be a valid S3 or FILE URI: must start with "s3://" or '
+                                 '"file://"')
         elif isinstance(input, s3_input):
             return input
         elif isinstance(input, file_input):
             return input
         else:
-            raise ValueError(
-                'Cannot format input {}. Expecting one of str, s3_input, or file_input'.format(
-                    input))
+            raise ValueError('Cannot format input {}. Expecting one of str, s3_input, or file_input'.format(input))
 
     @staticmethod
     def _format_record_set_list_input(inputs):

diff --git a/src/sagemaker/session.py b/src/sagemaker/session.py
@@ -371,6 +371,52 @@ def stop_tuning_job(self, name):
                 LOGGER.error('Error occurred while attempting to stop tuning job: {}. Please try again.'.format(name))
                 raise
 
+    def transform(self, job_name, model_name, strategy, max_concurrent_transforms, max_payload, env,
+                  input_config, output_config, resource_config, tags):
+        """Create an Amazon SageMaker transform job.
+
+        Args:
+            job_name (str): Name of the transform job being created.
+            model_name (str): Name of the SageMaker model being used for the transform job.
+            strategy (str): The strategy used to decide how to batch records in a single request.
+                Possible values are 'MULTI_RECORD' and 'SINGLE_RECORD'.
+            max_concurrent_transforms (int): The maximum number of HTTP requests to be made to
+                each individual transform container at one time.
+            max_payload (int): Maximum size of the payload in a single HTTP request to the container in MB.
+            env (dict): Environment variables to be set for use during the transform job.
+            input_config (dict): A dictionary describing the input data (and its location) for the job.
+            output_config (dict): A dictionary describing the output location for the job.
+            resource_config (dict): A dictionary describing the resources to complete the job.
+            tags (list[dict]): List of tags for labeling a training job. For more, see
+                https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
+        """
+        transform_request = {
+            'TransformJobName': job_name,
+            'ModelName': model_name,
+            'TransformInput': input_config,
+            'TransformOutput': output_config,
+            'TransformResources': resource_config,
+        }
+
+        if strategy is not None:
+            transform_request['BatchStrategy'] = strategy
+
+        if max_concurrent_transforms is not None:
+            transform_request['MaxConcurrentTransforms'] = max_concurrent_transforms
+
+        if max_payload is not None:
+            transform_request['MaxPayloadInMB'] = max_payload
+
+        if env is not None:
+            transform_request['Environment'] = env
+
+        if tags is not None:
+            transform_request['Tags'] = tags
+
+        LOGGER.info('Creating transform job with name: {}'.format(job_name))
+        LOGGER.debug('Transform request: {}'.format(json.dumps(transform_request, indent=4)))
+        self.sagemaker_client.create_transform_job(**transform_request)
+
     def create_model(self, name, role, primary_container):
         """Create an Amazon SageMaker ``Model``.
 
@@ -522,6 +568,23 @@ def wait_for_tuning_job(self, job, poll=5):
         self._check_job_status(job, desc, 'HyperParameterTuningJobStatus')
         return desc
 
+    def wait_for_transform_job(self, job, poll=5):
+        """Wait for an Amazon SageMaker transform job to complete.
+
+        Args:
+            job (str): Name of the transform job to wait for.
+            poll (int): Polling interval in seconds (default: 5).
+
+        Returns:
+            (dict): Return value from the ``DescribeTransformJob`` API.
+
+        Raises:
+            ValueError: If the transform job fails.
+        """
+        desc = _wait_until(lambda: _transform_job_status(self.sagemaker_client, job), poll)
+        self._check_job_status(job, desc, 'TransformJobStatus')
+        return desc
+
     def _check_job_status(self, job, desc, status_key_name):
         """Check to see if the job completed successfully and, if not, construct and
         raise a ValueError.
@@ -898,7 +961,7 @@ def __init__(self, s3_data, distribution='FullyReplicated', compression=None,
             compression (str): Valid values: 'Gzip', None (default: None). This is used only in Pipe input mode.
             content_type (str): MIME type of the input data (default: None).
             record_wrapping (str): Valid values: 'RecordIO' (default: None).
-            s3_data_type (str): Value values: 'S3Prefix', 'ManifestFile'. If 'S3Prefix', ``s3_data`` defines
+            s3_data_type (str): Valid values: 'S3Prefix', 'ManifestFile'. If 'S3Prefix', ``s3_data`` defines
                 a prefix of s3 objects to train on. All objects with s3 keys beginning with ``s3_data`` will
                 be used to train. If 'ManifestFile', then ``s3_data`` defines a single s3 manifest file, listing
                 each s3 object to train on. The Manifest file format is described in the SageMaker API documentation:
@@ -982,6 +1045,29 @@ def _tuning_job_status(sagemaker_client, job_name):
     return desc
 
 
+def _transform_job_status(sagemaker_client, job_name):
+    transform_job_status_codes = {
+        'Completed': '!',
+        'InProgress': '.',
+        'Failed': '*',
+        'Stopped': 's',
+        'Stopping': '_'
+    }
+    in_progress_statuses = ['InProgress', 'Stopping']
+
+    desc = sagemaker_client.describe_transform_job(TransformJobName=job_name)
+    status = desc['TransformJobStatus']
+
+    print(transform_job_status_codes.get(status, '?'), end='')
+    sys.stdout.flush()
+
+    if status in in_progress_statuses:
+        return None
+
+    print('')
+    return desc
+
+
 def _deploy_done(sagemaker_client, endpoint_name):
     hosting_status_codes = {
         "OutOfService": "x",