diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 33f3633766..b610592700 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -2,7 +2,11 @@
 CHANGELOG
 =========
 
-======
+1.11.3dev
+=========
+
+* feature: Local Mode: Add support for Batch Inference
+
 1.11.2
 ======
 
@@ -10,7 +14,6 @@ CHANGELOG
 * enhancement: Local Mode: accept short lived credentials with a warning message
 * bug-fix: Local Mode: pass in job name as parameter for training environment variable
 
-=======
 1.11.1
 ======
 
diff --git a/README.rst b/README.rst
index ce9e9e99bb..26f0472644 100644
--- a/README.rst
+++ b/README.rst
@@ -226,6 +226,27 @@ Here is an end-to-end example:
     predictor.delete_endpoint()
 
 
+If you don't want to deploy your model locally, you can also choose to perform a Local Batch Transform Job. This is
+useful if you want to test your container before creating a Sagemaker Batch Transform Job. Note that the performance
+will not match Batch Transform Jobs hosted on SageMaker but it is still a useful tool to ensure you have everything
+right or if you are not dealing with huge amounts of data.
+
+Here is an end-to-end example:
+
+.. code:: python
+
+    from sagemaker.mxnet import MXNet
+
+    mxnet_estimator = MXNet('train.py',
+                            train_instance_type='local',
+                            train_instance_count=1)
+
+    mxnet_estimator.fit('file:///tmp/my_training_data')
+    transformer = mxnet_estimator.transformer(1, 'local', assemble_with='Line', max_payload=1)
+    transformer.transform('s3://my/transform/data, content_type='text/csv', split_type='Line')
+    transformer.wait()
+
+
 For detailed examples of running Docker in local mode, see:
 
 - `TensorFlow local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_local_mode_mnist.ipynb>`__.
diff --git a/src/sagemaker/amazon/common.py b/src/sagemaker/amazon/common.py
index c15f6e344b..e15cc09a4d 100644
--- a/src/sagemaker/amazon/common.py
+++ b/src/sagemaker/amazon/common.py
@@ -153,7 +153,7 @@ def write_spmatrix_to_sparse_tensor(file, array, labels=None):
 def read_records(file):
     """Eagerly read a collection of amazon Record protobuf objects from file."""
     records = []
-    for record_data in _read_recordio(file):
+    for record_data in read_recordio(file):
         record = Record()
         record.ParseFromString(record_data)
         records.append(record)
@@ -183,7 +183,7 @@ def _write_recordio(f, data):
     f.write(padding[pad])
 
 
-def _read_recordio(f):
+def read_recordio(f):
     while(True):
         try:
             read_kmagic, = struct.unpack('I', f.read(4))
diff --git a/src/sagemaker/local/data.py b/src/sagemaker/local/data.py
new file mode 100644
index 0000000000..6c90467233
--- /dev/null
+++ b/src/sagemaker/local/data.py
@@ -0,0 +1,380 @@
+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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.
+from __future__ import absolute_import
+
+import os
+import sys
+import tempfile
+from abc import ABCMeta
+from abc import abstractmethod
+from six import with_metaclass
+
+from six.moves.urllib.parse import urlparse
+
+import sagemaker.amazon.common
+import sagemaker.local.utils
+import sagemaker.utils
+
+
+def get_data_source_instance(data_source, sagemaker_session):
+    """Return an Instance of :class:`sagemaker.local.data.DataSource` that can handle
+    the provided data_source URI.
+
+    data_source can be either file:// or s3://
+
+    Args:
+        data_source (str): a valid URI that points to a data source.
+        sagemaker_session (:class:`sagemaker.session.Session`): a SageMaker Session to interact with
+            S3 if required.
+
+    Returns
+        :class:`sagemaker.local.data.DataSource`: an Instance of a Data Source
+
+    """
+    parsed_uri = urlparse(data_source)
+    if parsed_uri.scheme == 'file':
+        return LocalFileDataSource(parsed_uri.path)
+    elif parsed_uri.scheme == 's3':
+        return S3DataSource(parsed_uri.netloc, parsed_uri.path, sagemaker_session)
+
+
+def get_splitter_instance(split_type):
+    """Return an Instance of :class:`sagemaker.local.data.Splitter` according to
+    the specified `split_type`.
+
+    Args:
+        split_type (str): either 'Line' or 'RecordIO'. Can be left as None to signal no data split
+            will happen.
+
+    Returns
+        :class:`sagemaker.local.data.Splitter`: an Instance of a Splitter
+    """
+    if split_type is None:
+        return NoneSplitter()
+    elif split_type == 'Line':
+        return LineSplitter()
+    elif split_type == 'RecordIO':
+        return RecordIOSplitter()
+    else:
+        raise ValueError('Invalid Split Type: %s' % split_type)
+
+
+def get_batch_strategy_instance(strategy, splitter):
+    """Return an Instance of :class:`sagemaker.local.data.BatchStrategy` according to `strategy`
+
+    Args:
+        strategy (str): Either 'SingleRecord' or 'MultiRecord'
+        splitter (:class:`sagemaker.local.data.Splitter): splitter to get the data from.
+
+    Returns
+        :class:`sagemaker.local.data.BatchStrategy`: an Instance of a BatchStrategy
+    """
+    if strategy == 'SingleRecord':
+        return SingleRecordStrategy(splitter)
+    elif strategy == 'MultiRecord':
+        return MultiRecordStrategy(splitter)
+    else:
+        raise ValueError('Invalid Batch Strategy: %s - Valid Strategies: "SingleRecord", "MultiRecord"')
+
+
+class DataSource(with_metaclass(ABCMeta, object)):
+
+    @abstractmethod
+    def get_file_list(self):
+        """Retrieve the list of absolute paths to all the files in this data source.
+
+        Returns:
+             List[str]: List of absolute paths.
+        """
+        pass
+
+    @abstractmethod
+    def get_root_dir(self):
+        """Retrieve the absolute path to the root directory of this data source.
+
+        Returns:
+            str: absolute path to the root directory of this data source.
+        """
+        pass
+
+
+class LocalFileDataSource(DataSource):
+    """Represents a data source within the local filesystem.
+    """
+
+    def __init__(self, root_path):
+        self.root_path = os.path.abspath(root_path)
+        if not os.path.exists(self.root_path):
+            raise RuntimeError('Invalid data source: %s does not exist.' % self.root_path)
+
+    def get_file_list(self):
+        """Retrieve the list of absolute paths to all the files in this data source.
+
+        Returns:
+             List[str] List of absolute paths.
+        """
+        if os.path.isdir(self.root_path):
+            return [os.path.join(self.root_path, f) for f in os.listdir(self.root_path)
+                    if os.path.isfile(os.path.join(self.root_path, f))]
+        else:
+            return [self.root_path]
+
+    def get_root_dir(self):
+        """Retrieve the absolute path to the root directory of this data source.
+
+        Returns:
+            str: absolute path to the root directory of this data source.
+        """
+        if os.path.isdir(self.root_path):
+            return self.root_path
+        else:
+            return os.path.dirname(self.root_path)
+
+
+class S3DataSource(DataSource):
+    """Defines a data source given by a bucket and S3 prefix. The contents will be downloaded
+    and then processed as local data.
+    """
+
+    def __init__(self, bucket, prefix, sagemaker_session):
+        """Create an S3DataSource instance
+
+        Args:
+            bucket (str): S3 bucket name
+            prefix (str): S3 prefix path to the data
+            sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker_session with the desired settings
+                to talk to S3
+        """
+
+        # Create a temporary dir to store the S3 contents
+        root_dir = sagemaker.utils.get_config_value('local.container_root', sagemaker_session.config)
+        if root_dir:
+            root_dir = os.path.abspath(root_dir)
+
+        working_dir = tempfile.mkdtemp(dir=root_dir)
+        sagemaker.utils.download_folder(bucket, prefix, working_dir, sagemaker_session)
+        self.files = LocalFileDataSource(working_dir)
+
+    def get_file_list(self):
+        """Retrieve the list of absolute paths to all the files in this data source.
+
+        Returns:
+             List[str]: List of absolute paths.
+        """
+        return self.files.get_file_list()
+
+    def get_root_dir(self):
+        """Retrieve the absolute path to the root directory of this data source.
+
+        Returns:
+            str: absolute path to the root directory of this data source.
+        """
+        return self.files.get_root_dir()
+
+
+class Splitter(with_metaclass(ABCMeta, object)):
+
+    @abstractmethod
+    def split(self, file):
+        """Split a file into records using a specific strategy
+
+        Args:
+            file (str): path to the file to split
+
+        Returns:
+            generator for the individual records that were split from the file
+        """
+        pass
+
+
+class NoneSplitter(Splitter):
+    """Does not split records, essentially reads the whole file.
+    """
+
+    def split(self, file):
+        """Split a file into records using a specific strategy.
+
+        For this NoneSplitter there is no actual split happening and the file is returned
+        as a whole.
+
+        Args:
+            file (str): path to the file to split
+
+        Returns: generator for the individual records that were split from the file
+        """
+        with open(file, 'r') as f:
+            yield f.read()
+
+
+class LineSplitter(Splitter):
+    """Split records by new line.
+    """
+
+    def split(self, file):
+        """Split a file into records using a specific strategy
+
+        This LineSplitter splits the file on each line break.
+
+        Args:
+            file (str): path to the file to split
+
+        Returns: generator for the individual records that were split from the file
+        """
+        with open(file, 'r') as f:
+            for line in f:
+                yield line
+
+
+class RecordIOSplitter(Splitter):
+    """Split using Amazon Recordio.
+
+    Not useful for string content.
+
+    """
+    def split(self, file):
+        """Split a file into records using a specific strategy
+
+        This RecordIOSplitter splits the data into individual RecordIO records.
+
+        Args:
+            file (str): path to the file to split
+
+        Returns: generator for the individual records that were split from the file
+        """
+        with open(file, 'rb') as f:
+            for record in sagemaker.amazon.common.read_recordio(f):
+                yield record
+
+
+class BatchStrategy(with_metaclass(ABCMeta, object)):
+
+    def __init__(self, splitter):
+        """Create a Batch Strategy Instance
+
+        Args:
+            splitter (:class:`sagemaker.local.data.Splitter`): A Splitter to pre-process the data
+                before batching.
+        """
+        self.splitter = splitter
+
+    @abstractmethod
+    def pad(self, file, size):
+        """Group together as many records as possible to fit in the specified size
+
+        Args:
+            file (str): file path to read the records from.
+            size (int): maximum size in MB that each group of records will be fitted to.
+                passing 0 means unlimited size.
+
+        Returns:
+            generator of records
+        """
+        pass
+
+
+class MultiRecordStrategy(BatchStrategy):
+    """Feed multiple records at a time for batch inference.
+
+    Will group up as many records as possible within the payload specified.
+    """
+    def __init__(self, splitter):
+        """Create a MultiRecordStrategy Instance
+
+        Args:
+            splitter (:class:`sagemaker.local.data.Splitter`): A Splitter to pre-process the data
+                before batching.
+        """
+        super(MultiRecordStrategy, self).__init__(splitter)
+
+    def pad(self, file, size=6):
+        """Group together as many records as possible to fit in the specified size
+
+        Args:
+            file (str): file path to read the records from.
+            size (int): maximum size in MB that each group of records will be fitted to.
+                passing 0 means unlimited size.
+
+        Returns:
+            generator of records
+        """
+        buffer = ''
+        for element in self.splitter.split(file):
+            if _payload_size_within_limit(buffer + element, size):
+                buffer += element
+            else:
+                tmp = buffer
+                buffer = element
+                yield tmp
+        if _validate_payload_size(buffer, size):
+            yield buffer
+
+
+class SingleRecordStrategy(BatchStrategy):
+    """Feed a single record at a time for batch inference.
+
+    If a single record does not fit within the payload specified it will throw a RuntimeError.
+    """
+    def __init__(self, splitter):
+        """Create a SingleRecordStrategy Instance
+
+        Args:
+            splitter (:class:`sagemaker.local.data.Splitter`): A Splitter to pre-process the data
+                before batching.
+        """
+        super(SingleRecordStrategy, self).__init__(splitter)
+
+    def pad(self, file, size=6):
+        """Group together as many records as possible to fit in the specified size
+
+        This SingleRecordStrategy will not group any record and will return them one by one as
+        long as they are within the maximum size.
+
+        Args:
+            file (str): file path to read the records from.
+            size (int): maximum size in MB that each group of records will be fitted to.
+                passing 0 means unlimited size.
+
+        Returns:
+             generator of records
+        """
+        for element in self.splitter.split(file):
+            if _validate_payload_size(element, size):
+                yield element
+
+
+def _payload_size_within_limit(payload, size):
+    size_in_bytes = size * 1024 * 1024
+    if size == 0:
+        return True
+    else:
+        return sys.getsizeof(payload) < size_in_bytes
+
+
+def _validate_payload_size(payload, size):
+    """Check if a payload is within the size in MB threshold. Raise an exception otherwise.
+
+    Args:
+        payload: data that will be checked
+        size (int): max size in MB
+
+    Returns:
+         bool: True if within bounds. if size=0 it will always return True
+
+    Raises:
+        RuntimeError: If the payload is larger a runtime error is thrown.
+    """
+
+    if _payload_size_within_limit(payload, size):
+        return True
+    else:
+        raise RuntimeError('Record is larger than %sMB. Please increase your max_payload' % size)
diff --git a/src/sagemaker/local/entities.py b/src/sagemaker/local/entities.py
index a67c14acb1..199db7d5cb 100644
--- a/src/sagemaker/local/entities.py
+++ b/src/sagemaker/local/entities.py
@@ -13,11 +13,16 @@
 from __future__ import absolute_import
 
 import datetime
+import json
 import logging
+import os
+import tempfile
 import time
 import urllib3
 
+import sagemaker.local.data
 from sagemaker.local.image import _SageMakerContainer
+from sagemaker.local.utils import copy_directory_structure, move_to_destination
 from sagemaker.utils import get_config_value
 
 logger = logging.getLogger(__name__)
@@ -76,6 +81,227 @@ def describe(self):
         return response
 
 
+class _LocalTransformJob(object):
+
+    _CREATING = 'Creating'
+    _COMPLETED = 'Completed'
+
+    def __init__(self, transform_job_name, model_name, local_session=None):
+        from sagemaker.local import LocalSession
+        self.local_session = local_session or LocalSession()
+        local_client = self.local_session.sagemaker_client
+
+        self.name = transform_job_name
+        self.model_name = model_name
+
+        # TODO - support SageMaker Models not just local models. This is not
+        # ideal but it may be a good thing to do.
+        self.primary_container = local_client.describe_model(model_name)['PrimaryContainer']
+        self.container = None
+        self.start_time = None
+        self.end_time = None
+        self.batch_strategy = None
+        self.environment = {}
+        self.state = _LocalTransformJob._CREATING
+
+    def start(self, input_data, output_data, transform_resources, **kwargs):
+        """Start the Local Transform Job
+
+        Args:
+            input_data (dict): Describes the dataset to be transformed and the location where it is stored.
+            output_data (dict): Identifies the location where to save the results from the transform job
+            transform_resources (dict): compute instances for the transform job. Currently only supports local or
+                local_gpu
+            **kwargs: additional arguments coming from the boto request object
+        """
+        self.transform_resources = transform_resources
+        self.input_data = input_data
+        self.output_data = output_data
+
+        image = self.primary_container['Image']
+        instance_type = transform_resources['InstanceType']
+        instance_count = 1
+
+        environment = self._get_container_environment(**kwargs)
+
+        # Start the container, pass the environment and wait for it to start up
+        self.container = _SageMakerContainer(instance_type, instance_count, image, self.local_session)
+        self.container.serve(self.primary_container['ModelDataUrl'], environment)
+
+        serving_port = get_config_value('local.serving_port', self.local_session.config) or 8080
+        _wait_for_serving_container(serving_port)
+
+        # Get capabilities from Container if needed
+        endpoint_url = 'http://localhost:%s/execution-parameters' % serving_port
+        response, code = _perform_request(endpoint_url)
+        if code == 200:
+            execution_parameters = json.loads(response.read())
+            # MaxConcurrentTransforms is ignored because we currently only support 1
+            for setting in ('BatchStrategy', 'MaxPayloadInMB'):
+                if setting not in kwargs and setting in execution_parameters:
+                    kwargs[setting] = execution_parameters[setting]
+
+        # Apply Defaults if none was provided
+        kwargs.update(self._get_required_defaults(**kwargs))
+
+        self.start_time = datetime.datetime.now()
+        self.batch_strategy = kwargs['BatchStrategy']
+        if 'Environment' in kwargs:
+            self.environment = kwargs['Environment']
+
+        # run the batch inference requests
+        self._perform_batch_inference(input_data, output_data, **kwargs)
+        self.end_time = datetime.datetime.now()
+        self.state = self._COMPLETED
+
+    def describe(self):
+        """Describe this _LocalTransformJob
+
+        The response is a JSON-like dictionary that follows the response of the
+        boto describe_transform_job() API.
+
+        Returns:
+            dict: description of this _LocalTransformJob
+        """
+        response = {
+            'TransformJobStatus': self.state,
+            'ModelName': self.model_name,
+            'TransformJobName': self.name,
+            'TransformJobArn': _UNUSED_ARN,
+            'TransformEndTime': self.end_time,
+            'CreationTime': self.start_time,
+            'TransformStartTime': self.start_time,
+            'Environment': {},
+            'BatchStrategy': self.batch_strategy,
+        }
+
+        if self.transform_resources:
+            response['TransformResources'] = self.transform_resources
+
+        if self.output_data:
+            response['TransformOutput'] = self.output_data
+
+        if self.input_data:
+            response['TransformInput'] = self.input_data
+
+        return response
+
+    def _get_container_environment(self, **kwargs):
+        """Get all the Environment variables that will be passed to the container
+
+        Certain input fields such as BatchStrategy have different values for the API vs the Environment
+        variables, such as SingleRecord vs SINGLE_RECORD. This method also handles this conversion.
+
+        Args:
+            **kwargs: existing transform arguments
+
+        Returns:
+            dict: All the environment variables that should be set in the container
+
+        """
+        environment = {}
+        environment.update(self.primary_container['Environment'])
+        environment['SAGEMAKER_BATCH'] = 'True'
+        if 'MaxPayloadInMB' in kwargs:
+            environment['SAGEMAKER_MAX_PAYLOAD_IN_MB'] = str(kwargs['MaxPayloadInMB'])
+
+        if 'BatchStrategy' in kwargs:
+            if kwargs['BatchStrategy'] == 'SingleRecord':
+                strategy_env_value = 'SINGLE_RECORD'
+            elif kwargs['BatchStrategy'] == 'MultiRecord':
+                strategy_env_value = 'MULTI_RECORD'
+            else:
+                raise ValueError('Invalid BatchStrategy, must be \'SingleRecord\' or \'MultiRecord\'')
+            environment['SAGEMAKER_BATCH_STRATEGY'] = strategy_env_value
+
+        # we only do 1 max concurrent transform in Local Mode
+        if 'MaxConcurrentTransforms' in kwargs and int(kwargs['MaxConcurrentTransforms']) > 1:
+            logger.warning('Local Mode only supports 1 ConcurrentTransform. Setting MaxConcurrentTransforms to 1')
+        environment['SAGEMAKER_MAX_CONCURRENT_TRANSFORMS'] = '1'
+
+        # if there were environment variables passed to the Transformer we will pass them to the
+        # container as well.
+        if 'Environment' in kwargs:
+            environment.update(kwargs['Environment'])
+        return environment
+
+    def _get_required_defaults(self, **kwargs):
+        """Return the default values for anything that was not provided by either the user or the container
+
+        Args:
+            **kwargs: current transform arguments
+
+        Returns:
+            dict: key/values for the default parameters that are missing.
+        """
+        defaults = {}
+        if 'BatchStrategy' not in kwargs:
+            defaults['BatchStrategy'] = 'MultiRecord'
+
+        if 'MaxPayloadInMB' not in kwargs:
+            defaults['MaxPayloadInMB'] = 6
+
+        return defaults
+
+    def _get_working_directory(self):
+        # Root dir to use for intermediate data location. To make things simple we will write here regardless
+        # of the final destination. At the end the files will either be moved or uploaded to S3 and deleted.
+        root_dir = get_config_value('local.container_root', self.local_session.config)
+        if root_dir:
+            root_dir = os.path.abspath(root_dir)
+
+        working_dir = tempfile.mkdtemp(dir=root_dir)
+        return working_dir
+
+    def _prepare_data_transformation(self, input_data, batch_strategy):
+        input_path = input_data['DataSource']['S3DataSource']['S3Uri']
+        data_source = sagemaker.local.data.get_data_source_instance(input_path, self.local_session)
+
+        split_type = input_data['SplitType'] if 'SplitType' in input_data else None
+        splitter = sagemaker.local.data.get_splitter_instance(split_type)
+
+        batch_provider = sagemaker.local.data.get_batch_strategy_instance(batch_strategy, splitter)
+        return data_source, batch_provider
+
+    def _perform_batch_inference(self, input_data, output_data, **kwargs):
+        # Transform the input data to feed the serving container. We need to first gather the files
+        # from S3 or Local FileSystem. Split them as required (Line, RecordIO, None) and finally batch them
+        # according to the batch strategy and limit the request size.
+
+        batch_strategy = kwargs['BatchStrategy']
+        max_payload = int(kwargs['MaxPayloadInMB'])
+        data_source, batch_provider = self._prepare_data_transformation(input_data, batch_strategy)
+
+        # Output settings
+        accept = output_data['Accept'] if 'Accept' in output_data else None
+
+        working_dir = self._get_working_directory()
+        dataset_dir = data_source.get_root_dir()
+
+        for file in data_source.get_file_list():
+
+            relative_path = os.path.dirname(os.path.relpath(file, dataset_dir))
+            filename = os.path.basename(file)
+            copy_directory_structure(working_dir, relative_path)
+            destination_path = os.path.join(working_dir, relative_path, filename + '.out')
+
+            with open(destination_path, 'wb') as f:
+                for item in batch_provider.pad(file, max_payload):
+                    # call the container and add the result to inference.
+                    response = self.local_session.sagemaker_runtime_client.invoke_endpoint(
+                        item, '', input_data['ContentType'], accept)
+
+                    response_body = response['Body']
+                    data = response_body.read().strip()
+                    response_body.close()
+                    f.write(data)
+                    if 'AssembleWith' in output_data and output_data['AssembleWith'] == 'Line':
+                        f.write(b'\n')
+
+        move_to_destination(working_dir, output_data['S3OutputPath'], self.local_session)
+        self.container.stop_serving()
+
+
 class _LocalModel(object):
 
     def __init__(self, model_name, primary_container):
@@ -143,29 +369,10 @@ def serve(self):
         self.container = _SageMakerContainer(instance_type, instance_count, image, self.local_session)
         self.container.serve(self.primary_container['ModelDataUrl'], self.primary_container['Environment'])
 
-        i = 0
-        http = urllib3.PoolManager()
         serving_port = get_config_value('local.serving_port', self.local_session.config) or 8080
-        endpoint_url = 'http://localhost:%s/ping' % serving_port
-        while True:
-            i += 1
-            if i >= HEALTH_CHECK_TIMEOUT_LIMIT:
-                self.state = _LocalEndpoint._FAILED
-                raise RuntimeError('Giving up, endpoint: %s didn\'t launch correctly' % self.name)
-
-            logger.info('Checking if endpoint is up, attempt: %s' % i)
-            try:
-                r = http.request('GET', endpoint_url)
-                if r.status != 200:
-                    logger.info('Container still not up, got: %s' % r.status)
-                else:
-                    # the container is running and it passed the healthcheck status is now InService
-                    self.state = _LocalEndpoint._IN_SERVICE
-                    return
-            except urllib3.exceptions.RequestError:
-                logger.info('Container still not up')
-
-            time.sleep(1)
+        _wait_for_serving_container(serving_port)
+        # the container is running and it passed the healthcheck status is now InService
+        self.state = _LocalEndpoint._IN_SERVICE
 
     def stop(self):
         if self.container:
@@ -181,3 +388,33 @@ def describe(self):
             'EndpointStatus': self.state
         }
         return response
+
+
+def _wait_for_serving_container(serving_port):
+    i = 0
+    http = urllib3.PoolManager()
+
+    endpoint_url = 'http://localhost:%s/ping' % serving_port
+    while True:
+        i += 1
+        if i >= HEALTH_CHECK_TIMEOUT_LIMIT:
+            raise RuntimeError('Giving up, endpoint didn\'t launch correctly')
+
+        logger.info('Checking if serving container is up, attempt: %s' % i)
+        response, code = _perform_request(endpoint_url, http)
+        if code != 200:
+            logger.info('Container still not up, got: %s' % code)
+        else:
+            return
+
+        time.sleep(1)
+
+
+def _perform_request(endpoint_url, pool_manager=None):
+    http = pool_manager or urllib3.PoolManager()
+    try:
+        r = http.request('GET', endpoint_url)
+        code = r.status
+    except urllib3.exceptions.RequestError:
+        return None, -1
+    return r, code
diff --git a/src/sagemaker/local/image.py b/src/sagemaker/local/image.py
index 1a845b38f6..58e8d7c637 100644
--- a/src/sagemaker/local/image.py
+++ b/src/sagemaker/local/image.py
@@ -33,7 +33,6 @@
 import yaml
 
 import sagemaker
-from sagemaker.utils import get_config_value
 
 CONTAINER_PREFIX = 'algo'
 DOCKER_COMPOSE_FILENAME = 'docker-compose.yaml'
@@ -217,9 +216,9 @@ def retrieve_artifacts(self, compose_data):
             for volume in volumes:
                 host_dir, container_dir = volume.split(':')
                 if container_dir == '/opt/ml/model':
-                    self._recursive_copy(host_dir, s3_model_artifacts)
+                    sagemaker.local.utils.recursive_copy(host_dir, s3_model_artifacts)
                 elif container_dir == '/opt/ml/output':
-                    self._recursive_copy(host_dir, s3_output_artifacts)
+                    sagemaker.local.utils.recursive_copy(host_dir, s3_output_artifacts)
 
         return s3_model_artifacts
 
@@ -252,49 +251,6 @@ def write_config_files(self, host, hyperparameters, input_data_config):
         _write_json_file(os.path.join(config_path, 'resourceconfig.json'), resource_config)
         _write_json_file(os.path.join(config_path, 'inputdataconfig.json'), json_input_data_config)
 
-    def _recursive_copy(self, src, dst):
-        for root, dirs, files in os.walk(src):
-            root = os.path.relpath(root, src)
-            current_path = os.path.join(src, root)
-            target_path = os.path.join(dst, root)
-
-            for file in files:
-                shutil.copy(os.path.join(current_path, file), os.path.join(target_path, file))
-            for dir in dirs:
-                new_dir = os.path.join(target_path, dir)
-                if not os.path.exists(new_dir):
-                    os.mkdir(os.path.join(target_path, dir))
-
-    def _download_folder(self, bucket_name, prefix, target):
-        boto_session = self.sagemaker_session.boto_session
-
-        s3 = boto_session.resource('s3')
-        bucket = s3.Bucket(bucket_name)
-
-        for obj_sum in bucket.objects.filter(Prefix=prefix):
-            # if obj_sum is a folder object skip it.
-            if obj_sum.key != '' and obj_sum.key[-1] == '/':
-                continue
-            obj = s3.Object(obj_sum.bucket_name, obj_sum.key)
-            s3_relative_path = obj_sum.key[len(prefix):].lstrip('/')
-            file_path = os.path.join(target, s3_relative_path)
-
-            try:
-                os.makedirs(os.path.dirname(file_path))
-            except OSError as exc:
-                if exc.errno != errno.EEXIST:
-                    raise
-                pass
-            obj.download_file(file_path)
-
-    def _download_file(self, bucket_name, path, target):
-        path = path.lstrip('/')
-        boto_session = self.sagemaker_session.boto_session
-
-        s3 = boto_session.resource('s3')
-        bucket = s3.Bucket(bucket_name)
-        bucket.download_file(path, target)
-
     def _prepare_training_volumes(self, data_dir, input_data_config, hyperparameters):
         shared_dir = os.path.join(self.container_root, 'shared')
         model_dir = os.path.join(self.container_root, 'model')
@@ -322,7 +278,7 @@ def _prepare_training_volumes(self, data_dir, input_data_config, hyperparameters
 
             if parsed_uri.scheme == 's3':
                 bucket_name = parsed_uri.netloc
-                self._download_folder(bucket_name, key, channel_dir)
+                sagemaker.utils.download_folder(bucket_name, key, channel_dir, self.sagemaker_session)
             elif parsed_uri.scheme == 'file':
                 path = parsed_uri.path
                 volumes.append(_Volume(path, channel=channel_name))
@@ -356,7 +312,7 @@ def _prepare_serving_volumes(self, model_location):
             parsed_uri = urlparse(model_location)
             filename = os.path.basename(parsed_uri.path)
             tar_location = os.path.join(container_model_dir, filename)
-            self._download_file(parsed_uri.netloc, parsed_uri.path, tar_location)
+            sagemaker.utils.download_file(parsed_uri.netloc, parsed_uri.path, tar_location, self.sagemaker_session)
 
             if tarfile.is_tarfile(tar_location):
                 with tarfile.open(tar_location) as tar:
@@ -460,8 +416,8 @@ def _create_docker_host(self, host, environment, optml_subdirs, command, volumes
         }
 
         if command == 'serve':
-            serving_port = get_config_value('local.serving_port',
-                                            self.sagemaker_session.config) or 8080
+            serving_port = sagemaker.utils.get_config_value('local.serving_port',
+                                                            self.sagemaker_session.config) or 8080
             host_config.update({
                 'ports': [
                     '%s:8080' % serving_port
@@ -471,7 +427,8 @@ def _create_docker_host(self, host, environment, optml_subdirs, command, volumes
         return host_config
 
     def _create_tmp_folder(self):
-        root_dir = get_config_value('local.container_root', self.sagemaker_session.config)
+        root_dir = sagemaker.utils.get_config_value('local.container_root',
+                                                    self.sagemaker_session.config)
         if root_dir:
             root_dir = os.path.abspath(root_dir)
 
diff --git a/src/sagemaker/local/local_session.py b/src/sagemaker/local/local_session.py
index 157eaf9b40..8361e18dab 100644
--- a/src/sagemaker/local/local_session.py
+++ b/src/sagemaker/local/local_session.py
@@ -20,7 +20,8 @@
 from botocore.exceptions import ClientError
 
 from sagemaker.local.image import _SageMakerContainer
-from sagemaker.local.entities import _LocalEndpointConfig, _LocalEndpoint, _LocalModel, _LocalTrainingJob
+from sagemaker.local.entities import (_LocalEndpointConfig, _LocalEndpoint, _LocalModel,
+                                      _LocalTrainingJob, _LocalTransformJob)
 from sagemaker.session import Session
 from sagemaker.utils import get_config_value
 
@@ -38,6 +39,7 @@ class LocalSagemakerClient(object):
     """
 
     _training_jobs = {}
+    _transform_jobs = {}
     _models = {}
     _endpoint_configs = {}
     _endpoints = {}
@@ -49,10 +51,7 @@ def __init__(self, sagemaker_session=None):
             sagemaker_session (sagemaker.session.Session): a session to use to read configurations
                 from, and use its boto client.
         """
-        self.serve_container = None
         self.sagemaker_session = sagemaker_session or LocalSession()
-        self.s3_model_artifacts = None
-        self.created_endpoint = False
 
     def create_training_job(self, TrainingJobName, AlgorithmSpecification, InputDataConfig, OutputDataConfig,
                             ResourceConfig, **kwargs):
@@ -80,7 +79,7 @@ def describe_training_job(self, TrainingJobName):
         """Describe a local training job.
 
         Args:
-            TrainingJobName (str): Not used in this implmentation.
+            TrainingJobName (str): Training job name to describe.
 
         Returns: (dict) DescribeTrainingJob Response.
 
@@ -91,6 +90,19 @@ def describe_training_job(self, TrainingJobName):
         else:
             return LocalSagemakerClient._training_jobs[TrainingJobName].describe()
 
+    def create_transform_job(self, TransformJobName, ModelName, TransformInput, TransformOutput,
+                             TransformResources, **kwargs):
+        transform_job = _LocalTransformJob(TransformJobName, ModelName, self.sagemaker_session)
+        LocalSagemakerClient._transform_jobs[TransformJobName] = transform_job
+        transform_job.start(TransformInput, TransformOutput, TransformResources, **kwargs)
+
+    def describe_transform_job(self, TransformJobName):
+        if TransformJobName not in LocalSagemakerClient._transform_jobs:
+            error_response = {'Error': {'Code': 'ValidationException', 'Message': 'Could not find local transform job'}}
+            raise ClientError(error_response, 'describe_transform_job')
+        else:
+            return LocalSagemakerClient._transform_jobs[TransformJobName].describe()
+
     def create_model(self, ModelName, PrimaryContainer, *args, **kwargs):
         """Create a Local Model Object
 
@@ -154,8 +166,14 @@ def __init__(self, config=None):
 
     def invoke_endpoint(self, Body, EndpointName, ContentType, Accept):
         url = "http://localhost:%s/invocations" % self.serving_port
+        headers = {
+            'Content-type': ContentType
+        }
+        if Accept is not None:
+            headers['Accept'] = Accept
+
         r = self.http.request('POST', url, body=Body, preload_content=False,
-                              headers={'Content-type': ContentType, 'Accept': Accept})
+                              headers=headers)
 
         return {'Body': r, 'ContentType': Accept}
 
diff --git a/src/sagemaker/local/utils.py b/src/sagemaker/local/utils.py
new file mode 100644
index 0000000000..355c4f77d2
--- /dev/null
+++ b/src/sagemaker/local/utils.py
@@ -0,0 +1,79 @@
+# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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.
+from __future__ import absolute_import
+
+import os
+import shutil
+
+from six.moves.urllib.parse import urlparse
+
+
+def copy_directory_structure(destination_directory, relative_path):
+    """Create all the intermediate directories required for relative_path to exist within destination_directory.
+    This assumes that relative_path is a directory located within root_dir.
+
+    Examples:
+        destination_directory: /tmp/destination
+        relative_path: test/unit/
+
+        will create:  /tmp/destination/test/unit
+
+    Args:
+        destination_directory (str): root of the destination directory where the directory structure will be created.
+        relative_path (str): relative path that will be created within destination_directory
+    """
+    full_path = os.path.join(destination_directory, relative_path)
+    if os.path.exists(full_path):
+        return
+
+    os.makedirs(destination_directory, relative_path)
+
+
+def move_to_destination(source, destination, sagemaker_session):
+    """move source to destination. Can handle uploading to S3
+
+    Args:
+        source (str): root directory to move
+        destination (str): file:// or s3:// URI that source will be moved to.
+        sagemaker_session (sagemaker.Session): a sagemaker_session to interact with S3 if needed
+    """
+    parsed_uri = urlparse(destination)
+    if parsed_uri.scheme == 'file':
+        recursive_copy(source, parsed_uri.path)
+    elif parsed_uri.scheme == 's3':
+        bucket = parsed_uri.netloc
+        path = parsed_uri.path.strip('/')
+        sagemaker_session.upload_data(source, bucket, path)
+    else:
+        raise ValueError('Invalid destination URI, must be s3:// or file://, got: %s' % destination)
+
+    shutil.rmtree(source)
+
+
+def recursive_copy(source, destination):
+    """Similar to shutil.copy but the destination directory can exist. Existing files will be overriden.
+    Args:
+        source (str): source path
+        destination (str): destination path
+    """
+    for root, dirs, files in os.walk(source):
+        root = os.path.relpath(root, source)
+        current_path = os.path.join(source, root)
+        target_path = os.path.join(destination, root)
+
+        for file in files:
+            shutil.copy(os.path.join(current_path, file), os.path.join(target_path, file))
+        for dir in dirs:
+            new_dir = os.path.join(target_path, dir)
+            if not os.path.exists(new_dir):
+                os.mkdir(os.path.join(target_path, dir))
diff --git a/src/sagemaker/transformer.py b/src/sagemaker/transformer.py
index a9c17afdd0..b3bb1d2303 100644
--- a/src/sagemaker/transformer.py
+++ b/src/sagemaker/transformer.py
@@ -31,7 +31,7 @@ def __init__(self, model_name, instance_count, instance_type, strategy=None, ass
             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'.
+                Valid values: 'MultiRecord' and 'SingleRecord'.
             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.
@@ -96,7 +96,8 @@ def transform(self, data, data_type='S3Prefix', content_type=None, compression_t
                 Valid values: 'None', 'Line', and 'RecordIO'.
             job_name (str): job name (default: None). If not specified, one will be generated.
         """
-        if not data.startswith('s3://'):
+        local_mode = self.sagemaker_session.local_mode
+        if not local_mode and not data.startswith('s3://'):
             raise ValueError('Invalid S3 URI: {}'.format(data))
 
         if job_name is not None:
diff --git a/src/sagemaker/utils.py b/src/sagemaker/utils.py
index f83c047cf2..c190987c31 100644
--- a/src/sagemaker/utils.py
+++ b/src/sagemaker/utils.py
@@ -12,10 +12,12 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import errno
+import os
+import re
 import sys
 import time
 
-import re
 from datetime import datetime
 from functools import wraps
 
@@ -193,6 +195,67 @@ def secondary_training_status_message(job_description, prev_description):
         return '\n'.join(status_strs)
 
 
+def download_folder(bucket_name, prefix, target, sagemaker_session):
+    """Download a folder from S3 to a local path
+
+    Args:
+        bucket_name (str): S3 bucket name
+        prefix (str): S3 prefix within the bucket that will be downloaded. Can be a single file.
+        target (str): destination path where the downloaded items will be placed
+        sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
+    """
+    boto_session = sagemaker_session.boto_session
+
+    s3 = boto_session.resource('s3')
+    bucket = s3.Bucket(bucket_name)
+
+    prefix = prefix.lstrip('/')
+
+    # there is a chance that the prefix points to a file and not a 'directory' if that is the case
+    # we should just download it.
+    objects = list(bucket.objects.filter(Prefix=prefix))
+
+    if len(objects) > 0 and objects[0].key == prefix and prefix[-1] != '/':
+        s3.Object(bucket_name, prefix).download_file(os.path.join(target, os.path.basename(prefix)))
+        return
+
+    # the prefix points to an s3 'directory' download the whole thing
+    for obj_sum in bucket.objects.filter(Prefix=prefix):
+        # if obj_sum is a folder object skip it.
+        if obj_sum.key != '' and obj_sum.key[-1] == '/':
+            continue
+        obj = s3.Object(obj_sum.bucket_name, obj_sum.key)
+        s3_relative_path = obj_sum.key[len(prefix):].lstrip('/')
+        file_path = os.path.join(target, s3_relative_path)
+
+        try:
+            os.makedirs(os.path.dirname(file_path))
+        except OSError as exc:
+            # EEXIST means the folder already exists, this is safe to skip
+            # anything else will be raised.
+            if exc.errno != errno.EEXIST:
+                raise
+            pass
+        obj.download_file(file_path)
+
+
+def download_file(bucket_name, path, target, sagemaker_session):
+    """Download a Single File from S3 into a local path
+
+    Args:
+        bucket_name (str): S3 bucket name
+        path (str): file path within the bucket
+        target (str): destination directory for the downloaded file.
+        sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
+    """
+    path = path.lstrip('/')
+    boto_session = sagemaker_session.boto_session
+
+    s3 = boto_session.resource('s3')
+    bucket = s3.Bucket(bucket_name)
+    bucket.download_file(path, target)
+
+
 class DeferredError(object):
     """Stores an exception and raises it at a later time anytime this
     object is accessed in any way.  Useful to allow soft-dependencies on imports,
diff --git a/tests/data/mxnet_mnist/transform/data.csv b/tests/data/mxnet_mnist/transform/data.csv
index 6e1e7b899d..6624d3fbc7 100644
--- a/tests/data/mxnet_mnist/transform/data.csv
+++ b/tests/data/mxnet_mnist/transform/data.csv
@@ -1 +1,11 @@
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0E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,2.313726E-01,9.764706E-01,9.960785E-01,2.431373E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,5.215687E-01,9.960785E-01,7.333333E-01,1.960784E-02,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,3.529412E-02,8.039216E-01,9.725491E-01,2.274510E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,4.941177E-01,9.960785E-01,7.137255E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,2.941177E-01,9.843138E-01,9.411765E-01,2.235294E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,7.450981E-02,8.666667E-01,9.960785E-01,6.509804E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,1.176471E-02,7.960785E-01,9.960785E-01,8.588236E-01,1.372549E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,1.490196E-01,9.960785E-01,9.960785E-01,3.019608E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,1.215686E-01,8.784314E-01,9.960785E-01,4.509804E-01,3.921569E-03,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,5.215687E-01,9.960785E-01,9.960785E-01,2.039216E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,2.392157E-01,9.490197E-01,9.960785E-01,9.960785E-01,2.039216E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,4.745098E-01,9.960785E-01,9.960785E-01,8.588236E-01,1.568628E-01,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,4.745098E-01,9.960785E-01,8.117648E-01,7.058824E-02,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.000000E+00,0.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diff --git a/tests/integ/test_local_mode.py b/tests/integ/test_local_mode.py
index e16f734d82..f3816f8b97 100644
--- a/tests/integ/test_local_mode.py
+++ b/tests/integ/test_local_mode.py
@@ -366,3 +366,33 @@ def test_mxnet_local_data_local_script():
         mx.delete_endpoint()
         time.sleep(5)
         fcntl.lockf(local_mode_lock, fcntl.LOCK_UN)
+
+
+@pytest.mark.continuous_testing
+def test_local_transform_mxnet(sagemaker_local_session, tmpdir):
+    data_path = os.path.join(DATA_DIR, 'mxnet_mnist')
+    script_path = os.path.join(data_path, 'mnist.py')
+
+    mx = MXNet(entry_point=script_path, role='SageMakerRole', train_instance_count=1,
+               train_instance_type='ml.c4.xlarge', sagemaker_session=sagemaker_local_session)
+
+    train_input = mx.sagemaker_session.upload_data(path=os.path.join(data_path, 'train'),
+                                                   key_prefix='integ-test-data/mxnet_mnist/train')
+    test_input = mx.sagemaker_session.upload_data(path=os.path.join(data_path, 'test'),
+                                                  key_prefix='integ-test-data/mxnet_mnist/test')
+
+    with timeout(minutes=15):
+        mx.fit({'train': train_input, 'test': test_input})
+
+    transform_input_path = os.path.join(data_path, 'transform')
+    transform_input_key_prefix = 'integ-test-data/mxnet_mnist/transform'
+    transform_input = mx.sagemaker_session.upload_data(path=transform_input_path,
+                                                       key_prefix=transform_input_key_prefix)
+
+    output_path = 'file://%s' % (str(tmpdir))
+    transformer = mx.transformer(1, 'local', assemble_with='Line', max_payload=1,
+                                 strategy='SingleRecord', output_path=output_path)
+    transformer.transform(transform_input, content_type='text/csv', split_type='Line')
+    transformer.wait()
+
+    assert os.path.exists(os.path.join(str(tmpdir), 'data.csv.out'))
diff --git a/tests/unit/test_common.py b/tests/unit/test_common.py
index ae693a32e3..d27ffd0890 100644
--- a/tests/unit/test_common.py
+++ b/tests/unit/test_common.py
@@ -17,7 +17,7 @@
 import pytest
 import itertools
 from scipy.sparse import coo_matrix
-from sagemaker.amazon.common import (record_deserializer, write_numpy_to_dense_tensor, _read_recordio,
+from sagemaker.amazon.common import (record_deserializer, write_numpy_to_dense_tensor, read_recordio,
                                      numpy_to_record_serializer, write_spmatrix_to_sparse_tensor)
 from sagemaker.amazon.record_pb2 import Record
 
@@ -26,7 +26,7 @@ def test_serializer():
     s = numpy_to_record_serializer()
     array_data = [[1.0, 2.0, 3.0], [10.0, 20.0, 30.0]]
     buf = s(np.array(array_data))
-    for record_data, expected in zip(_read_recordio(buf), array_data):
+    for record_data, expected in zip(read_recordio(buf), array_data):
         record = Record()
         record.ParseFromString(record_data)
         assert record.features["values"].float64_tensor.values == expected
@@ -36,7 +36,7 @@ def test_serializer_accepts_one_dimensional_array():
     s = numpy_to_record_serializer()
     array_data = [1.0, 2.0, 3.0]
     buf = s(np.array(array_data))
-    record_data = next(_read_recordio(buf))
+    record_data = next(read_recordio(buf))
     record = Record()
     record.ParseFromString(record_data)
     assert record.features["values"].float64_tensor.values == array_data
@@ -57,7 +57,7 @@ def test_float_write_numpy_to_dense_tensor():
     with tempfile.TemporaryFile() as f:
         write_numpy_to_dense_tensor(f, array)
         f.seek(0)
-        for record_data, expected in zip(_read_recordio(f), array_data):
+        for record_data, expected in zip(read_recordio(f), array_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].float64_tensor.values == expected
@@ -69,7 +69,7 @@ def test_float32_write_numpy_to_dense_tensor():
     with tempfile.TemporaryFile() as f:
         write_numpy_to_dense_tensor(f, array)
         f.seek(0)
-        for record_data, expected in zip(_read_recordio(f), array_data):
+        for record_data, expected in zip(read_recordio(f), array_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].float32_tensor.values == expected
@@ -81,7 +81,7 @@ def test_int_write_numpy_to_dense_tensor():
     with tempfile.TemporaryFile() as f:
         write_numpy_to_dense_tensor(f, array)
         f.seek(0)
-        for record_data, expected in zip(_read_recordio(f), array_data):
+        for record_data, expected in zip(read_recordio(f), array_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].int32_tensor.values == expected
@@ -94,7 +94,7 @@ def test_int_label():
     with tempfile.TemporaryFile() as f:
         write_numpy_to_dense_tensor(f, array, label_data)
         f.seek(0)
-        for record_data, expected, label in zip(_read_recordio(f), array_data, label_data):
+        for record_data, expected, label in zip(read_recordio(f), array_data, label_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].int32_tensor.values == expected
@@ -108,7 +108,7 @@ def test_float32_label():
     with tempfile.TemporaryFile() as f:
         write_numpy_to_dense_tensor(f, array, label_data)
         f.seek(0)
-        for record_data, expected, label in zip(_read_recordio(f), array_data, label_data):
+        for record_data, expected, label in zip(read_recordio(f), array_data, label_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].int32_tensor.values == expected
@@ -122,7 +122,7 @@ def test_float_label():
     with tempfile.TemporaryFile() as f:
         write_numpy_to_dense_tensor(f, array, label_data)
         f.seek(0)
-        for record_data, expected, label in zip(_read_recordio(f), array_data, label_data):
+        for record_data, expected, label in zip(read_recordio(f), array_data, label_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].int32_tensor.values == expected
@@ -154,7 +154,7 @@ def test_dense_float_write_spmatrix_to_sparse_tensor():
     with tempfile.TemporaryFile() as f:
         write_spmatrix_to_sparse_tensor(f, array)
         f.seek(0)
-        for record_data, expected_data, expected_keys in zip(_read_recordio(f), array_data, keys_data):
+        for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].float64_tensor.values == expected_data
@@ -169,7 +169,7 @@ def test_dense_float32_write_spmatrix_to_sparse_tensor():
     with tempfile.TemporaryFile() as f:
         write_spmatrix_to_sparse_tensor(f, array)
         f.seek(0)
-        for record_data, expected_data, expected_keys in zip(_read_recordio(f), array_data, keys_data):
+        for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].float32_tensor.values == expected_data
@@ -184,7 +184,7 @@ def test_dense_int_write_spmatrix_to_sparse_tensor():
     with tempfile.TemporaryFile() as f:
         write_spmatrix_to_sparse_tensor(f, array)
         f.seek(0)
-        for record_data, expected_data, expected_keys in zip(_read_recordio(f), array_data, keys_data):
+        for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].int32_tensor.values == expected_data
@@ -201,7 +201,7 @@ def test_dense_int_spmatrix_to_sparse_label():
         write_spmatrix_to_sparse_tensor(f, array, label_data)
         f.seek(0)
         for record_data, expected_data, expected_keys, label in zip(
-                _read_recordio(f),
+                read_recordio(f),
                 array_data,
                 keys_data,
                 label_data
@@ -223,7 +223,7 @@ def test_dense_float32_spmatrix_to_sparse_label():
         write_spmatrix_to_sparse_tensor(f, array, label_data)
         f.seek(0)
         for record_data, expected_data, expected_keys, label in zip(
-                _read_recordio(f),
+                read_recordio(f),
                 array_data,
                 keys_data,
                 label_data
@@ -245,7 +245,7 @@ def test_dense_float64_spmatrix_to_sparse_label():
         write_spmatrix_to_sparse_tensor(f, array, label_data)
         f.seek(0)
         for record_data, expected_data, expected_keys, label in zip(
-                _read_recordio(f),
+                read_recordio(f),
                 array_data,
                 keys_data,
                 label_data
@@ -281,7 +281,7 @@ def test_sparse_float_write_spmatrix_to_sparse_tensor():
     with tempfile.TemporaryFile() as f:
         write_spmatrix_to_sparse_tensor(f, array)
         f.seek(0)
-        for record_data, expected_data, expected_keys in zip(_read_recordio(f), array_data, keys_data):
+        for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].float64_tensor.values == expected_data
@@ -303,7 +303,7 @@ def test_sparse_float32_write_spmatrix_to_sparse_tensor():
     with tempfile.TemporaryFile() as f:
         write_spmatrix_to_sparse_tensor(f, array)
         f.seek(0)
-        for record_data, expected_data, expected_keys in zip(_read_recordio(f), array_data, keys_data):
+        for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].float32_tensor.values == expected_data
@@ -325,7 +325,7 @@ def test_sparse_int_write_spmatrix_to_sparse_tensor():
     with tempfile.TemporaryFile() as f:
         write_spmatrix_to_sparse_tensor(f, array)
         f.seek(0)
-        for record_data, expected_data, expected_keys in zip(_read_recordio(f), array_data, keys_data):
+        for record_data, expected_data, expected_keys in zip(read_recordio(f), array_data, keys_data):
             record = Record()
             record.ParseFromString(record_data)
             assert record.features["values"].int32_tensor.values == expected_data
diff --git a/tests/unit/test_image.py b/tests/unit/test_image.py
index d959329b5e..874a48b97c 100644
--- a/tests/unit/test_image.py
+++ b/tests/unit/test_image.py
@@ -10,6 +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.
+# language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
 import random
@@ -219,8 +220,8 @@ def test_check_output():
 @patch('sagemaker.local.image._stream_output')
 @patch('subprocess.Popen')
 @patch('sagemaker.local.image._SageMakerContainer._cleanup')
-@patch('sagemaker.local.image._SageMakerContainer._download_folder')
-def test_train(_download_folder, _cleanup, popen, _stream_output, LocalSession,
+@patch('sagemaker.utils.download_folder')
+def test_train(download_folder, _cleanup, popen, _stream_output, LocalSession,
                tmpdir, sagemaker_session):
 
     directories = [str(tmpdir.mkdir('container-root')), str(tmpdir.mkdir('data'))]
@@ -233,8 +234,8 @@ def test_train(_download_folder, _cleanup, popen, _stream_output, LocalSession,
         sagemaker_container.train(INPUT_DATA_CONFIG, HYPERPARAMETERS, TRAINING_JOB_NAME)
 
         channel_dir = os.path.join(directories[1], 'b')
-        download_folder_calls = [call('my-own-bucket', 'prefix', channel_dir)]
-        _download_folder.assert_has_calls(download_folder_calls)
+        download_folder_calls = [call('my-own-bucket', 'prefix', channel_dir, sagemaker_session)]
+        download_folder.assert_has_calls(download_folder_calls)
 
         docker_compose_file = os.path.join(sagemaker_container.container_root, 'docker-compose.yaml')
 
@@ -262,8 +263,8 @@ def test_train(_download_folder, _cleanup, popen, _stream_output, LocalSession,
 @patch('sagemaker.local.local_session.LocalSession')
 @patch('sagemaker.local.image._stream_output')
 @patch('sagemaker.local.image._SageMakerContainer._cleanup')
-@patch('sagemaker.local.image._SageMakerContainer._download_folder')
-def test_train_with_hyperparameters_without_job_name(_download_folder, _cleanup, _stream_output, LocalSession, tmpdir):
+@patch('sagemaker.utils.download_folder')
+def test_train_with_hyperparameters_without_job_name(download_folder, _cleanup, _stream_output, LocalSession, tmpdir):
 
     directories = [str(tmpdir.mkdir('container-root')), str(tmpdir.mkdir('data'))]
     with patch('sagemaker.local.image._SageMakerContainer._create_tmp_folder',
@@ -286,8 +287,8 @@ def test_train_with_hyperparameters_without_job_name(_download_folder, _cleanup,
 @patch('sagemaker.local.image._stream_output', side_effect=RuntimeError('this is expected'))
 @patch('subprocess.Popen')
 @patch('sagemaker.local.image._SageMakerContainer._cleanup')
-@patch('sagemaker.local.image._SageMakerContainer._download_folder')
-def test_train_error(_download_folder, _cleanup, popen, _stream_output, LocalSession, tmpdir, sagemaker_session):
+@patch('sagemaker.utils.download_folder')
+def test_train_error(download_folder, _cleanup, popen, _stream_output, LocalSession, tmpdir, sagemaker_session):
     directories = [str(tmpdir.mkdir('container-root')), str(tmpdir.mkdir('data'))]
 
     with patch('sagemaker.local.image._SageMakerContainer._create_tmp_folder', side_effect=directories):
@@ -305,8 +306,8 @@ def test_train_error(_download_folder, _cleanup, popen, _stream_output, LocalSes
 @patch('sagemaker.local.image._stream_output')
 @patch('subprocess.Popen')
 @patch('sagemaker.local.image._SageMakerContainer._cleanup')
-@patch('sagemaker.local.image._SageMakerContainer._download_folder')
-def test_train_local_code(_download_folder, _cleanup, popen, _stream_output,
+@patch('sagemaker.utils.download_folder')
+def test_train_local_code(download_folder, _cleanup, popen, _stream_output,
                           _local_session, tmpdir, sagemaker_session):
     directories = [str(tmpdir.mkdir('container-root')), str(tmpdir.mkdir('data'))]
     with patch('sagemaker.local.image._SageMakerContainer._create_tmp_folder',
@@ -391,11 +392,11 @@ def test_serve_local_code(up, copy, copytree, tmpdir, sagemaker_session):
                 assert '%s:/opt/ml/code' % '/tmp/code' in volumes
 
 
-@patch('sagemaker.local.image._SageMakerContainer._download_file')
+@patch('sagemaker.utils.download_file')
 @patch('tarfile.is_tarfile')
 @patch('tarfile.open', MagicMock())
 @patch('os.makedirs', Mock())
-def test_prepare_serving_volumes_with_s3_model(is_tarfile, _download_file, sagemaker_session):
+def test_prepare_serving_volumes_with_s3_model(is_tarfile, download_file, sagemaker_session):
 
     sagemaker_container = _SageMakerContainer('local', 1, 'some-image', sagemaker_session=sagemaker_session)
     sagemaker_container.container_root = '/tmp/container_root'
@@ -406,7 +407,7 @@ def test_prepare_serving_volumes_with_s3_model(is_tarfile, _download_file, sagem
     volumes = sagemaker_container._prepare_serving_volumes('s3://bucket/my_model.tar.gz')
 
     tar_location = os.path.join(container_model_dir, 'my_model.tar.gz')
-    _download_file.assert_called_with('bucket', '/my_model.tar.gz', tar_location)
+    download_file.assert_called_with('bucket', '/my_model.tar.gz', tar_location, sagemaker_session)
     is_tarfile.assert_called_with(tar_location)
 
     assert len(volumes) == 1
@@ -427,58 +428,6 @@ def test_prepare_serving_volumes_with_local_model(sagemaker_session):
     assert volumes[0].host_dir == '/path/to/my_model'
 
 
-@patch('os.makedirs')
-def test_download_folder(makedirs):
-    boto_mock = Mock(name='boto_session')
-    boto_mock.client('sts').get_caller_identity.return_value = {'Account': '123'}
-
-    session = sagemaker.Session(boto_session=boto_mock, sagemaker_client=Mock())
-
-    train_data = Mock()
-    validation_data = Mock()
-
-    train_data.bucket_name.return_value = BUCKET_NAME
-    train_data.key = '/prefix/train/train_data.csv'
-    validation_data.bucket_name.return_value = BUCKET_NAME
-    validation_data.key = '/prefix/train/validation_data.csv'
-
-    s3_files = [train_data, validation_data]
-    boto_mock.resource('s3').Bucket(BUCKET_NAME).objects.filter.return_value = s3_files
-
-    obj_mock = Mock()
-    boto_mock.resource('s3').Object.return_value = obj_mock
-
-    sagemaker_container = _SageMakerContainer('local', 2, 'my-image', sagemaker_session=session)
-    sagemaker_container._download_folder(BUCKET_NAME, '/prefix', '/tmp')
-
-    obj_mock.download_file.assert_called()
-    calls = [call(os.path.join('/tmp', 'train/train_data.csv')),
-             call(os.path.join('/tmp', 'train/validation_data.csv'))]
-    obj_mock.download_file.assert_has_calls(calls)
-    obj_mock.reset_mock()
-
-    # Testing with a trailing slash for the prefix.
-    sagemaker_container._download_folder(BUCKET_NAME, '/prefix/', '/tmp')
-    obj_mock.download_file.assert_called()
-    calls = [call(os.path.join('/tmp', 'train/train_data.csv')),
-             call(os.path.join('/tmp', 'train/validation_data.csv'))]
-
-    obj_mock.download_file.assert_has_calls(calls)
-
-
-def test_download_file():
-    boto_mock = Mock(name='boto_session')
-    boto_mock.client('sts').get_caller_identity.return_value = {'Account': '123'}
-    bucket_mock = Mock()
-    boto_mock.resource('s3').Bucket.return_value = bucket_mock
-    session = sagemaker.Session(boto_session=boto_mock, sagemaker_client=Mock())
-
-    sagemaker_container = _SageMakerContainer('local', 2, 'my-image', sagemaker_session=session)
-    sagemaker_container._download_file(BUCKET_NAME, '/prefix/path/file.tar.gz', '/tmp/file.tar.gz')
-
-    bucket_mock.download_file.assert_called_with('prefix/path/file.tar.gz', '/tmp/file.tar.gz')
-
-
 def test_ecr_login_non_ecr():
     session_mock = Mock()
     sagemaker.local.image._ecr_login_if_needed(session_mock, 'ubuntu')
diff --git a/tests/unit/test_local_data.py b/tests/unit/test_local_data.py
new file mode 100644
index 0000000000..1f0be09bf2
--- /dev/null
+++ b/tests/unit/test_local_data.py
@@ -0,0 +1,240 @@
+# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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.
+from __future__ import absolute_import
+
+import sys
+
+import pytest
+from mock import patch, Mock
+
+import sagemaker.amazon
+import sagemaker.local.data
+
+
+@patch('sagemaker.local.data.LocalFileDataSource')
+def test_get_data_source_instance_with_file(LocalFileDataSource, sagemaker_local_session):
+    # file
+    data_source = sagemaker.local.data.get_data_source_instance('file:///my/file', sagemaker_local_session)
+    LocalFileDataSource.assert_called_with('/my/file')
+    assert data_source is not None
+
+
+@patch('sagemaker.local.data.S3DataSource')
+def test_get_data_source_instance_with_s3(S3DataSource, sagemaker_local_session):
+    data_source = sagemaker.local.data.get_data_source_instance('s3://bucket/path', sagemaker_local_session)
+    S3DataSource.assert_called_with('bucket', '/path', sagemaker_local_session)
+    assert data_source is not None
+
+
+@patch('os.path.exists', Mock(return_value=True))
+@patch('os.path.abspath', lambda x: x)
+@patch('os.path.isdir', lambda x: x[-1] == '/')
+@patch('os.path.isfile', lambda x: x[-1] != '/')
+@patch('os.listdir')
+def test_file_data_source_get_file_list_with_folder(listdir):
+    data_source = sagemaker.local.data.LocalFileDataSource('/some/path/')
+    listdir.return_value = [
+        '/some/path/a',
+        '/some/path/b',
+        '/some/path/c/',
+        '/some/path/c/a'
+    ]
+    expected = [
+        '/some/path/a',
+        '/some/path/b',
+        '/some/path/c/a'
+    ]
+    result = data_source.get_file_list()
+    assert result == expected
+
+
+@patch('os.path.exists', Mock(return_value=True))
+@patch('os.path.abspath', lambda x: x)
+@patch('os.path.isdir', lambda x: x[-1] == '/')
+@patch('os.path.isfile', lambda x: x[-1] != '/')
+def test_file_data_source_get_file_list_with_single_file():
+    data_source = sagemaker.local.data.LocalFileDataSource('/some/batch/file.csv')
+    assert data_source.get_file_list() == ['/some/batch/file.csv']
+
+
+@patch('os.path.exists', Mock(return_value=True))
+@patch('os.path.abspath', lambda x: x)
+@patch('os.path.isdir', lambda x: x[-1] == '/')
+def test_file_data_source_get_root():
+    data_source = sagemaker.local.data.LocalFileDataSource('/some/path/')
+    assert data_source.get_root_dir() == '/some/path/'
+
+    data_source = sagemaker.local.data.LocalFileDataSource('/some/path/my_file.csv')
+    assert data_source.get_root_dir() == '/some/path'
+
+
+@patch('sagemaker.local.data.LocalFileDataSource')
+@patch('sagemaker.utils.download_folder')
+@patch('tempfile.mkdtemp', lambda dir: '/tmp/working_dir')
+def test_s3_data_source(download_folder, LocalFileDataSource, sagemaker_local_session):
+    data_source = sagemaker.local.data.S3DataSource('my_bucket', '/transform/data', sagemaker_local_session)
+    download_folder.assert_called()
+    data_source.get_file_list()
+    LocalFileDataSource().get_file_list.assert_called()
+    data_source.get_root_dir()
+    LocalFileDataSource().get_root_dir.assert_called()
+
+
+def test_get_splitter_instance_with_valid_types():
+    splitter = sagemaker.local.data.get_splitter_instance(None)
+    assert isinstance(splitter, sagemaker.local.data.NoneSplitter)
+
+    splitter = sagemaker.local.data.get_splitter_instance('Line')
+    assert isinstance(splitter, sagemaker.local.data.LineSplitter)
+
+    splitter = sagemaker.local.data.get_splitter_instance('RecordIO')
+    assert isinstance(splitter, sagemaker.local.data.RecordIOSplitter)
+
+
+def test_get_splitter_instance_with_invalid_types():
+    with pytest.raises(ValueError):
+        sagemaker.local.data.get_splitter_instance('SomethingInvalid')
+
+
+def test_none_splitter(tmpdir):
+    test_file_path = tmpdir.join('none_test.txt')
+
+    with test_file_path.open('w') as f:
+        f.write('this\nis\na\ntest')
+
+    splitter = sagemaker.local.data.NoneSplitter()
+    data = [x for x in splitter.split(str(test_file_path))]
+    assert data == ['this\nis\na\ntest']
+
+
+def test_line_splitter(tmpdir):
+    test_file_path = tmpdir.join('line_test.txt')
+
+    with test_file_path.open('w') as f:
+        for i in range(10):
+            f.write('%s\n' % i)
+
+    splitter = sagemaker.local.data.LineSplitter()
+    data = [x for x in splitter.split(str(test_file_path))]
+    assert len(data) == 10
+    for i in range(10):
+        assert data[i] == '%s\n' % str(i)
+
+
+def test_recordio_splitter(tmpdir):
+    test_file_path = tmpdir.join('recordio_test.txt')
+    with test_file_path.open('wb') as f:
+        for i in range(10):
+            data = str(i).encode('utf-8')
+            sagemaker.amazon.common._write_recordio(f, data)
+
+    splitter = sagemaker.local.data.RecordIOSplitter()
+    data = [x for x in splitter.split(str(test_file_path))]
+
+    assert len(data) == 10
+
+
+def test_get_batch_strategy_instance_with_valid_type():
+    # Single Record
+    strategy = sagemaker.local.data.get_batch_strategy_instance('SingleRecord', None)
+    assert isinstance(strategy, sagemaker.local.data.SingleRecordStrategy)
+
+    # Multi Record
+    strategy = sagemaker.local.data.get_batch_strategy_instance('MultiRecord', None)
+    assert isinstance(strategy, sagemaker.local.data.MultiRecordStrategy)
+
+
+def test_get_batch_strategy_instance_with_invalid_type():
+    with pytest.raises(ValueError):
+        # something invalid
+        sagemaker.local.data.get_batch_strategy_instance('NiceRecord', None)
+
+
+def test_single_record_strategy_with_small_records():
+    splitter = Mock()
+
+    single_record = sagemaker.local.data.SingleRecordStrategy(splitter)
+    data = ['123', '456', '789']
+    splitter.split.return_value = data
+
+    # given 3 small records the output should be the same 3 records
+    batch_records = [r for r in single_record.pad('some_file', 6)]
+    assert data == batch_records
+
+
+def test_single_record_strategy_with_large_records():
+    splitter = Mock()
+    mb = 1024 * 1024
+
+    single_record = sagemaker.local.data.SingleRecordStrategy(splitter)
+    # We will construct a huge record greater than 1MB and we expect an exception
+    # since there is no way to fit this with the payload size.
+    buffer = ''
+    while sys.getsizeof(buffer) < 2 * mb:
+        buffer += '1' * 100
+
+    data = [buffer]
+    with pytest.raises(RuntimeError):
+        splitter.split.return_value = data
+        batch_records = [r for r in single_record.pad('some_file', 1)]
+        print(batch_records)
+
+
+def test_single_record_strategy_with_no_payload_limit():
+    # passing 0 as the max_payload_size should work and a 1MB record should be returned
+    # correctly.
+    splitter = Mock()
+    mb = 1024 * 1024
+
+    buffer = ''
+    while sys.getsizeof(buffer) < 2 * mb:
+        buffer += '1' * 100
+    splitter.split.return_value = [buffer]
+
+    single_record = sagemaker.local.data.SingleRecordStrategy(splitter)
+    batch_records = [r for r in single_record.pad('some_file', 0)]
+    assert len(batch_records) == 1
+
+
+def test_multi_record_strategy_with_small_records():
+    splitter = Mock()
+
+    multi_record = sagemaker.local.data.MultiRecordStrategy(splitter)
+    data = ['123', '456', '789']
+    splitter.split.return_value = data
+
+    # given 3 small records, the output should be 1 single record with the data from all 3 combined
+    batch_records = [r for r in multi_record.pad('some_file', 6)]
+    assert len(batch_records) == 1
+    assert batch_records[0] == '123456789'
+
+
+def test_multi_record_strategy_with_large_records():
+    splitter = Mock()
+    mb = 1024 * 1024
+
+    multi_record = sagemaker.local.data.MultiRecordStrategy(splitter)
+    # we will construct several large records and we expect them to be merged into <1MB ones
+    buffer = ''
+    while sys.getsizeof(buffer) < 0.5 * mb:
+        buffer += '1' * 100
+
+    # buffer should be aprox 0.5 MB. We will make the data total 10 MB made out of 0.5mb records
+    # with a max_payload size of 1MB the expectation is to have ~10 output records.
+
+    data = [buffer for _ in range(10)]
+    splitter.split.return_value = data
+
+    batch_records = [r for r in multi_record.pad('some_file', 1)]
+    # check with 11 because there may be a bit of leftover.
+    assert len(batch_records) <= 11
diff --git a/tests/unit/test_local_entities.py b/tests/unit/test_local_entities.py
new file mode 100644
index 0000000000..86180951dd
--- /dev/null
+++ b/tests/unit/test_local_entities.py
@@ -0,0 +1,165 @@
+# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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.
+from __future__ import absolute_import
+
+import os
+import pytest
+
+from mock import patch, Mock
+
+import sagemaker.local
+
+
+@pytest.fixture(scope='session')
+def local_transform_job(sagemaker_local_session):
+    with patch('sagemaker.local.local_session.LocalSagemakerClient.describe_model') as describe_model:
+        describe_model.return_value = {'PrimaryContainer': {'Environment': {}, 'Image': 'some-image:1.0'}}
+        job = sagemaker.local.entities._LocalTransformJob('my-transform-job', 'some-model', sagemaker_local_session)
+        return job
+
+
+@patch('sagemaker.local.local_session.LocalSagemakerClient.describe_model', Mock(return_value={'PrimaryContainer': {}}))
+def test_local_transform_job_init(sagemaker_local_session):
+    job = sagemaker.local.entities._LocalTransformJob('my-transform-job', 'some-model', sagemaker_local_session)
+    assert job.name == 'my-transform-job'
+    assert job.state == sagemaker.local.entities._LocalTransformJob._CREATING
+
+
+def test_local_transform_job_container_environment(local_transform_job):
+    transform_kwargs = {
+        'MaxPayloadInMB': 3,
+        'BatchStrategy': 'MultiRecord',
+    }
+    container_env = local_transform_job._get_container_environment(**transform_kwargs)
+
+    assert 'SAGEMAKER_BATCH' in container_env
+    assert 'SAGEMAKER_MAX_PAYLOAD_IN_MB' in container_env
+    assert 'SAGEMAKER_BATCH_STRATEGY' in container_env
+    assert 'SAGEMAKER_MAX_CONCURRENT_TRANSFORMS' in container_env
+
+    transform_kwargs = {
+        'BatchStrategy': 'SingleRecord',
+    }
+
+    container_env = local_transform_job._get_container_environment(**transform_kwargs)
+
+    assert 'SAGEMAKER_BATCH' in container_env
+    assert 'SAGEMAKER_BATCH_STRATEGY' in container_env
+    assert 'SAGEMAKER_MAX_CONCURRENT_TRANSFORMS' in container_env
+
+    transform_kwargs = {
+        'Environment': {'MY_ENV': 3}
+    }
+
+    container_env = local_transform_job._get_container_environment(**transform_kwargs)
+
+    assert 'SAGEMAKER_BATCH' in container_env
+    assert 'SAGEMAKER_MAX_PAYLOAD_IN_MB' not in container_env
+    assert 'SAGEMAKER_BATCH_STRATEGY' not in container_env
+    assert 'SAGEMAKER_MAX_CONCURRENT_TRANSFORMS' in container_env
+    assert 'MY_ENV' in container_env
+
+
+def test_local_transform_job_defaults_with_empty_args(local_transform_job):
+    transform_kwargs = {}
+    defaults = local_transform_job._get_required_defaults(**transform_kwargs)
+    assert 'BatchStrategy' in defaults
+    assert 'MaxPayloadInMB' in defaults
+
+
+def test_local_transform_job_defaults_with_batch_strategy(local_transform_job):
+    transform_kwargs = {'BatchStrategy': 'my-own'}
+    defaults = local_transform_job._get_required_defaults(**transform_kwargs)
+    assert 'BatchStrategy' not in defaults
+    assert 'MaxPayloadInMB' in defaults
+
+
+def test_local_transform_job_defaults_with_max_payload(local_transform_job):
+    transform_kwargs = {'MaxPayloadInMB': 322}
+    defaults = local_transform_job._get_required_defaults(**transform_kwargs)
+    assert 'BatchStrategy' in defaults
+    assert 'MaxPayloadInMB' not in defaults
+
+
+@patch('sagemaker.local.entities._SageMakerContainer', Mock())
+@patch('sagemaker.local.entities._wait_for_serving_container', Mock())
+@patch('sagemaker.local.entities._perform_request')
+@patch('sagemaker.local.entities._LocalTransformJob._perform_batch_inference')
+def test_start_local_transform_job(_perform_batch_inference, _perform_request, local_transform_job):
+    input_data = {}
+    output_data = {}
+    transform_resources = {'InstanceType': 'local'}
+
+    response = Mock()
+    _perform_request.return_value = (response, 200)
+    response.read.return_value = '{"BatchStrategy": "SingleRecord"}'
+    local_transform_job.primary_container['ModelDataUrl'] = 'file:///some/model'
+    local_transform_job.start(input_data, output_data, transform_resources, Environment={})
+
+    _perform_batch_inference.assert_called()
+    response = local_transform_job.describe()
+    assert response['TransformJobStatus'] == 'Completed'
+
+
+@patch('sagemaker.local.data.get_batch_strategy_instance')
+@patch('sagemaker.local.data.get_data_source_instance')
+@patch('sagemaker.local.entities.move_to_destination')
+@patch('sagemaker.local.entities.get_config_value')
+def test_local_transform_job_perform_batch_inference(get_config_value, move_to_destination, get_data_source_instance,
+                                                     get_batch_strategy_instance, local_transform_job, tmpdir):
+    input_data = {
+        'DataSource': {
+            'S3DataSource': {
+                'S3Uri': 's3://some_bucket/nice/data'
+            }
+        },
+        'ContentType': 'text/csv'
+    }
+
+    output_data = {
+        'S3OutputPath': 's3://bucket/output',
+        'AssembleWith': 'Line'
+    }
+
+    transform_kwargs = {
+        'MaxPayloadInMB': 3,
+        'BatchStrategy': 'MultiRecord',
+    }
+
+    data_source = Mock()
+    data_source.get_file_list.return_value = ['/tmp/file1', '/tmp/file2']
+    data_source.get_root_dir.return_value = '/tmp'
+    get_data_source_instance.return_value = data_source
+
+    batch_strategy = Mock()
+    batch_strategy.pad.return_value = 'some data'
+    get_batch_strategy_instance.return_value = batch_strategy
+
+    get_config_value.return_value = str(tmpdir)
+
+    runtime_client = Mock()
+    response_object = Mock()
+    response_object.read.return_value = b'data'
+    runtime_client.invoke_endpoint.return_value = {'Body': response_object}
+    local_transform_job.local_session.sagemaker_runtime_client = runtime_client
+
+    local_transform_job.container = Mock()
+
+    local_transform_job._perform_batch_inference(input_data, output_data, **transform_kwargs)
+
+    dir, output, session = move_to_destination.call_args[0]
+    assert output == 's3://bucket/output'
+    output_files = os.listdir(dir)
+    assert len(output_files) == 2
+    assert 'file1.out' in output_files
+    assert 'file2.out' in output_files
diff --git a/tests/unit/test_local_session.py b/tests/unit/test_local_session.py
index 83e20a4cbf..78d9b49e77 100644
--- a/tests/unit/test_local_session.py
+++ b/tests/unit/test_local_session.py
@@ -172,6 +172,27 @@ def test_describe_model(LocalSession):
     assert response['PrimaryContainer']['ModelDataUrl'] == '/some/model/path'
 
 
+@patch('sagemaker.local.local_session._LocalTransformJob')
+@patch('sagemaker.local.local_session.LocalSession')
+def test_create_transform_job(LocalSession, _LocalTransformJob):
+    local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
+
+    local_sagemaker_client.create_transform_job('transform-job', 'some-model', None, None, None)
+    _LocalTransformJob().start.assert_called_with(None, None, None)
+
+    local_sagemaker_client.describe_transform_job('transform-job')
+    _LocalTransformJob().describe.assert_called()
+
+
+@patch('sagemaker.local.local_session._LocalTransformJob')
+@patch('sagemaker.local.local_session.LocalSession')
+def test_describe_transform_job_does_not_exist(LocalSession, _LocalTransformJob):
+    local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
+
+    with pytest.raises(ClientError):
+        local_sagemaker_client.describe_transform_job('transform-job-does-not-exist')
+
+
 @patch('sagemaker.local.local_session.LocalSession')
 def test_describe_endpoint_config(LocalSession):
     local_sagemaker_client = sagemaker.local.local_session.LocalSagemakerClient()
diff --git a/tests/unit/test_local_utils.py b/tests/unit/test_local_utils.py
new file mode 100644
index 0000000000..a4f2bb35fb
--- /dev/null
+++ b/tests/unit/test_local_utils.py
@@ -0,0 +1,37 @@
+# Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# 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.
+from __future__ import absolute_import
+
+import pytest
+from mock import patch, Mock
+
+import sagemaker
+
+BUCKET_NAME = 'some-nice-bucket'
+
+
+@patch('shutil.rmtree', Mock())
+@patch('sagemaker.local.utils.recursive_copy')
+def test_move_to_destination(recursive_copy):
+    # local files will just be recursive copied
+    sagemaker.local.utils.move_to_destination('/tmp/data', 'file:///target/dir/', None)
+    recursive_copy.assert_called()
+
+    # s3 destination will upload to S3
+    sms = Mock()
+    sagemaker.local.utils.move_to_destination('/tmp/data', 's3://bucket/path', sms)
+    sms.upload_data.assert_called()
+
+    # weird destination, should raise an exception
+    with pytest.raises(ValueError):
+        sagemaker.local.utils.move_to_destination('/tmp/data', 'ftp://ftp/in/2018', None)
diff --git a/tests/unit/test_transformer.py b/tests/unit/test_transformer.py
index 200d5df52d..1342cf5817 100644
--- a/tests/unit/test_transformer.py
+++ b/tests/unit/test_transformer.py
@@ -43,7 +43,7 @@
 @pytest.fixture()
 def sagemaker_session():
     boto_mock = Mock(name='boto_session')
-    return Mock(name='sagemaker_session', boto_session=boto_mock)
+    return Mock(name='sagemaker_session', boto_session=boto_mock, local_mode=False)
 
 
 @pytest.fixture()
diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py
index d8b0bc67ec..0caa7ae6df 100644
--- a/tests/unit/test_utils.py
+++ b/tests/unit/test_utils.py
@@ -14,17 +14,21 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+from datetime import datetime
+import os
+import time
+
 import pytest
-from mock import patch
+from mock import call, patch, Mock
 
+import sagemaker
 from sagemaker.utils import get_config_value, name_from_base,\
     to_str, DeferredError, extract_name_from_job_arn, secondary_training_status_changed,\
     secondary_training_status_message
 
-from datetime import datetime
-import time
 
 NAME = 'base_name'
+BUCKET_NAME = 'some_bucket'
 
 
 def test_get_config_value():
@@ -166,3 +170,80 @@ def test_secondary_training_status_message_prev_missing():
         MESSAGE
     )
     assert secondary_training_status_message(TRAINING_JOB_DESCRIPTION_1, {}) == expected
+
+
+@patch('os.makedirs')
+def test_download_folder(makedirs):
+    boto_mock = Mock(name='boto_session')
+    boto_mock.client('sts').get_caller_identity.return_value = {'Account': '123'}
+
+    session = sagemaker.Session(boto_session=boto_mock, sagemaker_client=Mock())
+
+    train_data = Mock()
+    validation_data = Mock()
+
+    train_data.bucket_name.return_value = BUCKET_NAME
+    train_data.key = 'prefix/train/train_data.csv'
+    validation_data.bucket_name.return_value = BUCKET_NAME
+    validation_data.key = 'prefix/train/validation_data.csv'
+
+    s3_files = [train_data, validation_data]
+    boto_mock.resource('s3').Bucket(BUCKET_NAME).objects.filter.return_value = s3_files
+
+    obj_mock = Mock()
+    boto_mock.resource('s3').Object.return_value = obj_mock
+
+    # all the S3 mocks are set, the test itself begins now.
+    sagemaker.utils.download_folder(BUCKET_NAME, '/prefix', '/tmp', session)
+
+    obj_mock.download_file.assert_called()
+    calls = [call(os.path.join('/tmp', 'train/train_data.csv')),
+             call(os.path.join('/tmp', 'train/validation_data.csv'))]
+    obj_mock.download_file.assert_has_calls(calls)
+    obj_mock.reset_mock()
+
+    # Testing with a trailing slash for the prefix.
+    sagemaker.utils.download_folder(BUCKET_NAME, '/prefix/', '/tmp', session)
+    obj_mock.download_file.assert_called()
+    obj_mock.download_file.assert_has_calls(calls)
+
+
+@patch('os.makedirs')
+def test_download_folder_points_to_single_file(makedirs):
+    boto_mock = Mock(name='boto_session')
+    boto_mock.client('sts').get_caller_identity.return_value = {'Account': '123'}
+
+    session = sagemaker.Session(boto_session=boto_mock, sagemaker_client=Mock())
+
+    train_data = Mock()
+
+    train_data.bucket_name.return_value = BUCKET_NAME
+    train_data.key = 'prefix/train/train_data.csv'
+
+    s3_files = [train_data]
+    boto_mock.resource('s3').Bucket(BUCKET_NAME).objects.filter.return_value = s3_files
+
+    obj_mock = Mock()
+    boto_mock.resource('s3').Object.return_value = obj_mock
+
+    # all the S3 mocks are set, the test itself begins now.
+    sagemaker.utils.download_folder(BUCKET_NAME, '/prefix/train/train_data.csv', '/tmp', session)
+
+    obj_mock.download_file.assert_called()
+    calls = [call(os.path.join('/tmp', 'train_data.csv'))]
+    obj_mock.download_file.assert_has_calls(calls)
+    assert boto_mock.resource('s3').Bucket(BUCKET_NAME).objects.filter.call_count == 1
+    obj_mock.reset_mock()
+
+
+def test_download_file():
+    boto_mock = Mock(name='boto_session')
+    boto_mock.client('sts').get_caller_identity.return_value = {'Account': '123'}
+    bucket_mock = Mock()
+    boto_mock.resource('s3').Bucket.return_value = bucket_mock
+    session = sagemaker.Session(boto_session=boto_mock, sagemaker_client=Mock())
+
+    sagemaker.utils.download_file(BUCKET_NAME, '/prefix/path/file.tar.gz',
+                                  '/tmp/file.tar.gz', session)
+
+    bucket_mock.download_file.assert_called_with('prefix/path/file.tar.gz', '/tmp/file.tar.gz')