Add transformer method to PipelineModel (aws#676)

This enables creating multi-container Transform jobs
with PipelineModel without having already
created a SageMaker multi-container Model.

Author: andremoeller

CHANGELOG.rst

@@ -2,13 +2,17 @@
 CHANGELOG
 =========
 
+1.18.5dev
+======
+
+* feature: ``PipelineModel``: Create a Transformer from a PipelineModel
 
 1.18.4
 ======
 
 * doc-fix: Remove incorrect parameter for EI TFS Python README
 * feature: ``Predictor``: delete SageMaker model
-* feature: ``Pipeline``: delete SageMaker model
+* feature: ``PipelineModel``: delete SageMaker model
 * bug-fix: Estimator.attach works with training jobs without hyperparameters
 * doc-fix: remove duplicate content from mxnet/README.rst
 * doc-fix: move overview content in main README into sphynx project

README.rst

@@ -965,8 +965,9 @@ the ML Pipeline.
    endpoint_name = 'inference-pipeline-endpoint'
    sm_model = PipelineModel(name=model_name, role=sagemaker_role, models=[sparkml_model, xgb_model])
 
-This will define a ``PipelineModel`` consisting of SparkML model and an XGBoost model stacked sequentially. For more
-information about how to train an XGBoost model, please refer to the XGBoost notebook here_.
+This will define a ``PipelineModel`` consisting of SparkML model and an XGBoost model stacked sequentially.
+
+For more information about how to train an XGBoost model, please refer to the XGBoost notebook here_.
 
 .. _here: https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html#xgboost-sample-notebooks
 
@@ -978,6 +979,37 @@ This returns a predictor the same way an ``Estimator`` does when ``deploy()`` is
 request using this predictor, you should pass the data that the first container expects and the predictor will return the
 output from the last container.
 
+You can also use a ``PipelineModel`` to create Transform Jobs for batch transformations. Using the same ``PipelineModel`` ``sm_model`` as above:
+
+.. code:: python
+
+   # Only instance_type and instance_count are required.
+   transformer = sm_model.transformer(instance_type='ml.c5.xlarge',
+                                      instance_count=1,
+                                      strategy='MultiRecord',
+                                      max_payload=6,
+                                      max_concurrent_transforms=8,
+                                      accept='text/csv',
+                                      assemble_with='Line',
+                                      output_path='s3://my-output-bucket/path/to/my/output/data/')
+   # Only data is required.
+   transformer.transform(data='s3://my-input-bucket/path/to/my/csv/data',
+                         content_type='text/csv',
+                         split_type='Line')
+   # Waits for the Pipeline Transform Job to finish.
+   transformer.wait()
+
+This runs a transform job against all the files under ``s3://mybucket/path/to/my/csv/data``, transforming the input
+data in order with each model container in the pipeline. For each input file that was successfully transformed, one output file in ``s3://my-output-bucket/path/to/my/output/data/``
+will be created with the same name, appended with '.out'.
+
+This transform job will split CSV files by newline separators, which is especially useful if the input files are large. The Transform Job will
+assemble the outputs with line separators when writing each input file's corresponding output file.
+
+Each payload entering the first model container will be up to six megabytes, and up to eight inference requests will be sent at the
+same time to the first model container. Since each payload will consist of a mini-batch of multiple CSV records, the model
+containers will transform each mini-batch of records.
+
 For comprehensive examples on how to use Inference Pipelines please refer to the following notebooks:
 
 - `inference_pipeline_sparkml_xgboost_abalone.ipynb <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/inference_pipeline_sparkml_xgboost_abalone/inference_pipeline_sparkml_xgboost_abalone.ipynb>`__

src/sagemaker/model.py

@@ -102,7 +102,9 @@ def _create_sagemaker_model(self, instance_type, accelerator_type=None):
         Args:
             instance_type (str): The EC2 instance type that this Model will be used for, this is only
                 used to determine if the image needs GPU support or not.
-            accelerator_type (str): <put docs here>
+            accelerator_type (str): Type of Elastic Inference accelerator to attach to an endpoint for model loading
+                and inference, for example, 'ml.eia1.medium'. If not specified, no Elastic Inference accelerator
+                will be attached to the endpoint.
         """
         container_def = self.prepare_container_def(instance_type, accelerator_type=accelerator_type)
         self.name = self.name or utils.name_from_image(container_def['Image'])
@@ -292,10 +294,6 @@ def transformer(self, instance_count, instance_type, strategy=None, assemble_wit
             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.
-            role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``, which is also used during
-                transform jobs. If not specified, the role from the Model will be used.
-            model_server_workers (int): Optional. The number of worker processes used by the inference server.
-                If None, server will use one worker per vCPU.
             volume_kms_key (str): Optional. KMS key ID for encrypting the volume attached to the ML
                 compute instance (default: None).
         """

src/sagemaker/pipeline.py

@@ -15,6 +15,7 @@
 import sagemaker
 from sagemaker.session import Session
 from sagemaker.utils import name_from_image
+from sagemaker.transformer import Transformer
 
 
 class PipelineModel(object):
@@ -104,6 +105,56 @@ def deploy(self, initial_instance_count, instance_type, endpoint_name=None, tags
         if self.predictor_cls:
             return self.predictor_cls(self.endpoint_name, self.sagemaker_session)
 
+    def _create_sagemaker_pipeline_model(self, instance_type):
+        """Create a SageMaker Model Entity
+
+        Args:
+            instance_type (str): The EC2 instance type that this Model will be used for, this is only
+                used to determine if the image needs GPU support or not.
+            accelerator_type (str): Type of Elastic Inference accelerator to attach to an endpoint for model loading
+                and inference, for example, 'ml.eia1.medium'. If not specified, no Elastic Inference accelerator
+                will be attached to the endpoint.
+        """
+        if not self.sagemaker_session:
+            self.sagemaker_session = Session()
+
+        containers = self.pipeline_container_def(instance_type)
+
+        self.name = self.name or name_from_image(containers[0]['Image'])
+        self.sagemaker_session.create_model(self.name, self.role, containers, vpc_config=self.vpc_config)
+
+    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, volume_kms_key=None):
+        """Return a ``Transformer`` that uses this Model.
+
+        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.
+            volume_kms_key (str): Optional. KMS key ID for encrypting the volume attached to the ML
+                compute instance (default: None).
+        """
+        self._create_sagemaker_pipeline_model(instance_type)
+
+        return Transformer(self.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.name,
+                           volume_kms_key=volume_kms_key, sagemaker_session=self.sagemaker_session)
+
     def delete_model(self):
         """Delete the SageMaker model backing this pipeline model. This does not delete the list of SageMaker models used
         in multiple containers to build the inference pipeline.

tests/data/sparkml_xgboost_pipeline/invalid_input.csv

@@ -0,0 +1 @@
+1.0,28.0,C,38.0,71.5,1.0

tests/data/sparkml_xgboost_pipeline/valid_input.csv

@@ -0,0 +1 @@
+1.0,C,38.0,71.5,1.0,female

tests/integ/test_inference_pipeline.py

@@ -16,8 +16,8 @@
 import os
 
 import pytest
-from tests.integ import DATA_DIR
-from tests.integ.timeout import timeout_and_delete_endpoint_by_name
+from tests.integ import DATA_DIR, TRANSFORM_DEFAULT_TIMEOUT_MINUTES
+from tests.integ.timeout import timeout_and_delete_endpoint_by_name, timeout_and_delete_model_with_transformer
 
 from sagemaker.amazon.amazon_estimator import get_image_uri
 from sagemaker.content_types import CONTENT_TYPE_CSV
@@ -27,6 +27,74 @@
 from sagemaker.sparkml.model import SparkMLModel
 from sagemaker.utils import sagemaker_timestamp
 
+SPARKML_DATA_PATH = os.path.join(DATA_DIR, 'sparkml_model')
+XGBOOST_DATA_PATH = os.path.join(DATA_DIR, 'xgboost_model')
+SPARKML_XGBOOST_DATA_DIR = 'sparkml_xgboost_pipeline'
+VALID_DATA_PATH = os.path.join(DATA_DIR, SPARKML_XGBOOST_DATA_DIR, 'valid_input.csv')
+INVALID_DATA_PATH = os.path.join(DATA_DIR, SPARKML_XGBOOST_DATA_DIR, 'invalid_input.csv')
+SCHEMA = json.dumps({
+    "input": [
+        {
+            "name": "Pclass",
+            "type": "float"
+        },
+        {
+            "name": "Embarked",
+            "type": "string"
+        },
+        {
+            "name": "Age",
+            "type": "float"
+        },
+        {
+            "name": "Fare",
+            "type": "float"
+        },
+        {
+            "name": "SibSp",
+            "type": "float"
+        },
+        {
+            "name": "Sex",
+            "type": "string"
+        }
+    ],
+    "output": {
+        "name": "features",
+        "struct": "vector",
+        "type": "double"
+    }
+})
+
+
+@pytest.mark.continuous_testing
+@pytest.mark.regional_testing
+def test_inference_pipeline_batch_transform(sagemaker_session):
+    sparkml_model_data = sagemaker_session.upload_data(
+        path=os.path.join(SPARKML_DATA_PATH, 'mleap_model.tar.gz'),
+        key_prefix='integ-test-data/sparkml/model')
+    xgb_model_data = sagemaker_session.upload_data(
+        path=os.path.join(XGBOOST_DATA_PATH, 'xgb_model.tar.gz'),
+        key_prefix='integ-test-data/xgboost/model')
+    batch_job_name = 'test-inference-pipeline-batch-{}'.format(sagemaker_timestamp())
+    sparkml_model = SparkMLModel(model_data=sparkml_model_data,
+                                 env={'SAGEMAKER_SPARKML_SCHEMA': SCHEMA},
+                                 sagemaker_session=sagemaker_session)
+    xgb_image = get_image_uri(sagemaker_session.boto_region_name, 'xgboost')
+    xgb_model = Model(model_data=xgb_model_data, image=xgb_image,
+                      sagemaker_session=sagemaker_session)
+    model = PipelineModel(models=[sparkml_model, xgb_model], role='SageMakerRole',
+                          sagemaker_session=sagemaker_session, name=batch_job_name)
+    transformer = model.transformer(1, 'ml.m4.xlarge')
+    transform_input_key_prefix = 'integ-test-data/sparkml_xgboost/transform'
+    transform_input = transformer.sagemaker_session.upload_data(path=VALID_DATA_PATH,
+                                                                key_prefix=transform_input_key_prefix)
+
+    with timeout_and_delete_model_with_transformer(transformer, sagemaker_session,
+                                                   minutes=TRANSFORM_DEFAULT_TIMEOUT_MINUTES):
+        transformer.transform(transform_input, content_type=CONTENT_TYPE_CSV, job_name=batch_job_name)
+        transformer.wait()
+
 
 @pytest.mark.canary_quick
 @pytest.mark.regional_testing
@@ -40,42 +108,10 @@ def test_inference_pipeline_model_deploy(sagemaker_session):
     xgb_model_data = sagemaker_session.upload_data(
         path=os.path.join(xgboost_data_path, 'xgb_model.tar.gz'),
         key_prefix='integ-test-data/xgboost/model')
-    schema = json.dumps({
-        "input": [
-            {
-                "name": "Pclass",
-                "type": "float"
-            },
-            {
-                "name": "Embarked",
-                "type": "string"
-            },
-            {
-                "name": "Age",
-                "type": "float"
-            },
-            {
-                "name": "Fare",
-                "type": "float"
-            },
-            {
-                "name": "SibSp",
-                "type": "float"
-            },
-            {
-                "name": "Sex",
-                "type": "string"
-            }
-        ],
-        "output": {
-            "name": "features",
-            "struct": "vector",
-            "type": "double"
-        }
-    })
+
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         sparkml_model = SparkMLModel(model_data=sparkml_model_data,
-                                     env={'SAGEMAKER_SPARKML_SCHEMA': schema},
+                                     env={'SAGEMAKER_SPARKML_SCHEMA': SCHEMA},
                                      sagemaker_session=sagemaker_session)
         xgb_image = get_image_uri(sagemaker_session.boto_region_name, 'xgboost')
         xgb_model = Model(model_data=xgb_model_data, image=xgb_image,
@@ -87,11 +123,13 @@ def test_inference_pipeline_model_deploy(sagemaker_session):
                                       serializer=json_serializer, content_type=CONTENT_TYPE_CSV,
                                       accept=CONTENT_TYPE_CSV)
 
-        valid_data = '1.0,C,38.0,71.5,1.0,female'
-        assert predictor.predict(valid_data) == "0.714013934135"
+        with open(VALID_DATA_PATH, 'r') as f:
+            valid_data = f.read()
+            assert predictor.predict(valid_data) == '0.714013934135'
 
-        invalid_data = "1.0,28.0,C,38.0,71.5,1.0"
-        assert (predictor.predict(invalid_data) is None)
+        with open(INVALID_DATA_PATH, 'r') as f:
+            invalid_data = f.read()
+            assert (predictor.predict(invalid_data) is None)
 
     model.delete_model()
     with pytest.raises(Exception) as exception:

tests/unit/test_pipeline_model.py

@@ -111,17 +111,58 @@ def test_deploy_endpoint_name(tfo, time, sagemaker_session):
     framework_model = DummyFrameworkModel(sagemaker_session)
     sparkml_model = SparkMLModel(model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session)
     model = PipelineModel(models=[framework_model, sparkml_model], role=ROLE, sagemaker_session=sagemaker_session)
-    model.deploy(endpoint_name='blah', instance_type=INSTANCE_TYPE, initial_instance_count=55)
+    model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1)
     sagemaker_session.endpoint_from_production_variants.assert_called_with(
-        'blah',
+        'mi-1-2017-10-10-14-14-15',
         [{'InitialVariantWeight': 1,
           'ModelName': 'mi-1-2017-10-10-14-14-15',
           'InstanceType': INSTANCE_TYPE,
-          'InitialInstanceCount': 55,
+          'InitialInstanceCount': 1,
           'VariantName': 'AllTraffic'}],
         None)
 
 
+@patch('tarfile.open')
+@patch('time.strftime', return_value=TIMESTAMP)
+def test_transformer(tfo, time, sagemaker_session):
+    framework_model = DummyFrameworkModel(sagemaker_session)
+    sparkml_model = SparkMLModel(model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session)
+    model_name = 'ModelName'
+    model = PipelineModel(models=[framework_model, sparkml_model], role=ROLE, sagemaker_session=sagemaker_session,
+                          name=model_name)
+
+    instance_count = 55
+    strategy = 'MultiRecord'
+    assemble_with = 'Line'
+    output_path = "s3://output/path"
+    output_kms_key = "output:kms:key"
+    accept = "application/jsonlines"
+    env = {"my_key": "my_value"}
+    max_concurrent_transforms = 20
+    max_payload = 5
+    tags = [{"my_tag": "my_value"}]
+    volume_kms_key = "volume:kms:key"
+    transformer = model.transformer(instance_type=INSTANCE_TYPE, instance_count=instance_count,
+                                    strategy=strategy, assemble_with=assemble_with, output_path=output_path,
+                                    output_kms_key=output_kms_key, accept=accept, env=env,
+                                    max_concurrent_transforms=max_concurrent_transforms,
+                                    max_payload=max_payload, tags=tags, volume_kms_key=volume_kms_key
+                                    )
+    assert transformer.instance_type == INSTANCE_TYPE
+    assert transformer.instance_count == instance_count
+    assert transformer.strategy == strategy
+    assert transformer.assemble_with == assemble_with
+    assert transformer.output_path == output_path
+    assert transformer.output_kms_key == output_kms_key
+    assert transformer.accept == accept
+    assert transformer.env == env
+    assert transformer.max_concurrent_transforms == max_concurrent_transforms
+    assert transformer.max_payload == max_payload
+    assert transformer.tags == tags
+    assert transformer.volume_kms_key == volume_kms_key
+    assert transformer.model_name == model_name
+
+
 @patch('tarfile.open')
 @patch('time.strftime', return_value=TIMESTAMP)
 def test_deploy_tags(tfo, time, sagemaker_session):