feature: repack_model support dependencies and code location

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result
 
 

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result
 
 

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result
 
 

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result
 
 

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result
 
 

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result
 
 

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result
 
 

src/sagemaker/model.py

@@ -448,21 +448,27 @@ def _upload_code(self, key_prefix, repack=False):
  local_code = utils.get_config_value('local.local_code', self.sagemaker_session.config)
  if self.sagemaker_session.local_mode and local_code:
  self.uploaded_code = None
- else:
- if not repack:
- bucket = self.bucket or self.sagemaker_session.default_bucket()
- self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
- bucket=bucket,
- s3_key_prefix=key_prefix,
- script=self.entry_point,
- directory=self.source_dir,
- dependencies=self.dependencies)
+ elif not repack:
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ self.uploaded_code = fw_utils.tar_and_upload_dir(session=self.sagemaker_session.boto_session,
+ bucket=bucket,
+ s3_key_prefix=key_prefix,
+ script=self.entry_point,
+ directory=self.source_dir,
+ dependencies=self.dependencies)
 
  if repack:
- self.repacked_model_data = utils.repack_model(inference_script=self.entry_point,
- source_directory=self.source_dir,
- model_uri=self.model_data,
- sagemaker_session=self.sagemaker_session)
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ repacked_model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ utils.repack_model(inference_script=self.entry_point,
+ source_directory=self.source_dir,
+ dependencies=self.dependencies,
+ model_uri=self.model_data,
+ repacked_model_uri=repacked_model_data,
+ sagemaker_session=self.sagemaker_session)
+
+ self.repacked_model_data = repacked_model_data
  self.uploaded_code = UploadedCode(s3_prefix=self.repacked_model_data,
  script_name=os.path.basename(self.entry_point))
 

src/sagemaker/mxnet/model.py

@@ -92,21 +92,21 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  Returns:
  dict[str, str]: A container definition object usable with the CreateModel API.
  """
-mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
+is_mms_version = parse_version(self.framework_version) >= parse_version(self._LOWEST_MMS_VERSION)
 
  deploy_image = self.image
  if not deploy_image:
  region_name = self.sagemaker_session.boto_session.region_name
 
  framework_name = self.__framework_name__
- if mms_version:
+ if is_mms_version:
  framework_name += '-serving'
 
  deploy_image = create_image_uri(region_name, framework_name, instance_type,
  self.framework_version, self.py_version, accelerator_type=accelerator_type)
 
  deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
- self._upload_code(deploy_key_prefix, mms_version)
+ self._upload_code(deploy_key_prefix, is_mms_version)
  deploy_env = dict(self.env)
  deploy_env.update(self._framework_env_vars())
 

src/sagemaker/tensorflow/serving.py

@@ -13,6 +13,7 @@
 from __future__ import absolute_import
 
 import logging
+import os
 
 import sagemaker
 from sagemaker.content_types import CONTENT_TYPE_JSON
@@ -128,10 +129,17 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
  env = self._get_container_env()
 
  if self.entry_point:
- model_data = sagemaker.utils.repack_model(self.entry_point,
- self.source_dir,
- self.model_data,
- self.sagemaker_session)
+ key_prefix = sagemaker.fw_utils.model_code_key_prefix(self.key_prefix, self.name, image)
+
+ bucket = self.bucket or self.sagemaker_session.default_bucket()
+ model_data = 's3://' + os.path.join(bucket, key_prefix, 'model.tar.gz')
+
+ sagemaker.utils.repack_model(self.entry_point,
+ self.source_dir,
+ self.dependencies,
+ self.model_data,
+ model_data,
+ self.sagemaker_session)
  else:
  model_data = self.model_data
 

src/sagemaker/utils.py

@@ -29,8 +29,6 @@
 
 import six
 
-import sagemaker
-
 ECR_URI_PATTERN = r'^(\d+)(\.)dkr(\.)ecr(\.)(.+)(\.)(amazonaws.com|c2s.ic.gov)(/)(.*:.*)$'
 
 
@@ -300,7 +298,12 @@ def _tmpdir(suffix='', prefix='tmp'):
  shutil.rmtree(tmp)
 
 
-def repack_model(inference_script, source_directory, model_uri, sagemaker_session):
+def repack_model(inference_script,
+ source_directory,
+ dependencies,
+ model_uri,
+ repacked_model_uri,
+ sagemaker_session):
  """Unpack model tarball and creates a new model tarball with the provided code script.
 
  This function does the following:
@@ -311,60 +314,91 @@ def repack_model(inference_script, source_directory, model_uri, sagemaker_sessio
  Args:
  inference_script (str): path or basename of the inference script that will be packed into the model
  source_directory (str): path including all the files that will be packed into the model
+ dependencies (list[str]): A list of paths to directories (absolute or relative) with
+ any additional libraries that will be exported to the container (default: []).
+ The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
+ Example:
+
+ The following call
+ >>> Estimator(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
+ results in the following inside the container:
+
+ >>> $ ls
+
+ >>> opt/ml/code
+ >>> |------ train.py
+ >>> |------ common
+ >>> |------ virtual-env
+
+ repacked_model_uri (str): path or file system location where the new model will be saved
  model_uri (str): S3 or file system location of the original model tar
  sagemaker_session (:class:`sagemaker.session.Session`): a sagemaker session to interact with S3.
 
  Returns:
  str: path to the new packed model
  """
-new_model_name = 'model-%s.tar.gz' % sagemaker.utils.sagemaker_short_timestamp()
+dependencies = dependencies or []
 
  with _tmpdir() as tmp:
- tmp_model_dir = os.path.join(tmp, 'model')
- os.mkdir(tmp_model_dir)
+ model_dir = _extract_model(model_uri, sagemaker_session, tmp)
 
- model_from_s3 = model_uri.lower().startswith('s3://')
- if model_from_s3:
- local_model_path = os.path.join(tmp, 'tar_file')
- download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ _create_or_update_code_dir(model_dir, inference_script, source_directory, dependencies, sagemaker_session, tmp)
 
- new_model_path = os.path.join(tmp, new_model_name)
- else:
- local_model_path = model_uri.replace('file://', '')
- new_model_path = os.path.join(os.path.dirname(local_model_path), new_model_name)
+ tmp_model_path = os.path.join(tmp, 'temp-model.tar.gz')
+ with tarfile.open(tmp_model_path, mode='w:gz') as t:
+ t.add(model_dir, arcname=os.path.sep)
 
- with tarfile.open(name=local_model_path, mode='r:gz') as t:
- t.extractall(path=tmp_model_dir)
+ _save_model(repacked_model_uri, tmp_model_path, sagemaker_session)
 
- code_dir = os.path.join(tmp_model_dir, 'code')
- if os.path.exists(code_dir):
- shutil.rmtree(code_dir, ignore_errors=True)
 
- if source_directory and source_directory.lower().startswith('s3://'):
- local_code_path = os.path.join(tmp, 'local_code.tar.gz')
- download_file_from_url(source_directory, local_code_path, sagemaker_session)
+def _save_model(repacked_model_uri, tmp_model_path, sagemaker_session):
+ if repacked_model_uri.lower().startswith('s3://'):
+ url = parse.urlparse(repacked_model_uri)
+ bucket, key = url.netloc, url.path.lstrip('/')
+ new_key = key.replace(os.path.basename(key), os.path.basename(repacked_model_uri))
 
- with tarfile.open(name=local_code_path, mode='r:gz') as t:
- t.extractall(path=code_dir)
+ sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(
+ tmp_model_path)
+ else:
+ shutil.move(tmp_model_path, repacked_model_uri.replace('file://', ''))
 
- elif source_directory:
- shutil.copytree(source_directory, code_dir)
- else:
- os.mkdir(code_dir)
- shutil.copy2(inference_script, code_dir)
 
- with tarfile.open(new_model_path, mode='w:gz') as t:
- t.add(tmp_model_dir, arcname=os.path.sep)
+def _create_or_update_code_dir(model_dir, inference_script, source_directory,
+ dependencies, sagemaker_session, tmp):
+ code_dir = os.path.join(model_dir, 'code')
+ if os.path.exists(code_dir):
+ shutil.rmtree(code_dir, ignore_errors=True)
+ if source_directory and source_directory.lower().startswith('s3://'):
+ local_code_path = os.path.join(tmp, 'local_code.tar.gz')
+ download_file_from_url(source_directory, local_code_path, sagemaker_session)
+
+ with tarfile.open(name=local_code_path, mode='r:gz') as t:
+ t.extractall(path=code_dir)
 
- if model_from_s3:
- url = parse.urlparse(model_uri)
- bucket, key = url.netloc, url.path.lstrip('/')
- new_key = key.replace(os.path.basename(key), new_model_name)
+ elif source_directory:
+ shutil.copytree(source_directory, code_dir)
+ else:
+ os.mkdir(code_dir)
+ shutil.copy2(inference_script, code_dir)
 
- sagemaker_session.boto_session.resource('s3').Object(bucket, new_key).upload_file(new_model_path)
- return 's3://%s/%s' % (bucket, new_key)
+ for dependency in dependencies:
+ if os.path.isdir(dependency):
+ shutil.copytree(dependency, code_dir)
  else:
- return 'file://%s' % new_model_path
+ shutil.copy2(dependency, code_dir)
+
+
+def _extract_model(model_uri, sagemaker_session, tmp):
+ tmp_model_dir = os.path.join(tmp, 'model')
+ os.mkdir(tmp_model_dir)
+ if model_uri.lower().startswith('s3://'):
+ local_model_path = os.path.join(tmp, 'tar_file')
+ download_file_from_url(model_uri, local_model_path, sagemaker_session)
+ else:
+ local_model_path = model_uri.replace('file://', '')
+ with tarfile.open(name=local_model_path, mode='r:gz') as t:
+ t.extractall(path=tmp_model_dir)
+ return tmp_model_dir
 
 
 def download_file_from_url(url, dst, sagemaker_session):

tests/data/tfs/tfs-test-entrypoint-and-dependencies/dependency.py

@@ -0,0 +1,12 @@
+# Copyright 2019 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.

tests/data/tfs/tfs-test-entrypoint-and-dependencies/inference.py

@@ -0,0 +1,27 @@
+# Copyright 2019 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.
+import json
+
+import dependency
+
+def input_handler(data, context):
+ data = json.loads(data.read().decode('utf-8'))
+ new_values = [x + 1 for x in data['instances']]
+ dumps = json.dumps({'instances': new_values})
+ return dumps
+
+
+def output_handler(data, context):
+ response_content_type = context.accept_header
+ prediction = data.content
+ return prediction, response_content_type

tests/data/tfs/tfs-test-model-with-inference/code/inference.py

@@ -1,4 +1,4 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# Copyright 2019 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
@@ -12,7 +12,6 @@
 # language governing permissions and limitations under the License.
 import json
 
-
 def input_handler(data, context):
  data = json.loads(data.read().decode('utf-8'))
  new_values = [x + 1 for x in data['instances']]

tests/integ/test_tfs.py

@@ -84,8 +84,8 @@ def tfs_predictor_with_model_and_entry_point_same_tar(instance_type,
 
 
 @pytest.fixture(scope='module')
-def tfs_predictor_with_model_and_entry_point_separated(instance_type,
- sagemaker_session, tf_full_version):
+def tfs_predictor_with_model_and_entry_point_and_dependencies(instance_type,
+sagemaker_session, tf_full_version):
  endpoint_name = sagemaker.utils.unique_name_from_base('sagemaker-tensorflow-serving')
 
  model_data = sagemaker_session.upload_data(
@@ -96,10 +96,14 @@ def tfs_predictor_with_model_and_entry_point_separated(instance_type,
  with tests.integ.timeout.timeout_and_delete_endpoint_by_name(endpoint_name,
  sagemaker_session):
  entry_point = os.path.join(tests.integ.DATA_DIR,
- 'tfs/tfs-test-model-with-inference/code/inference.py')
+ 'tfs/tfs-test-entrypoint-and-dependencies/inference.py')
+ dependencies = [os.path.join(tests.integ.DATA_DIR,
+ 'tfs/tfs-test-entrypoint-and-dependencies/dependency.py')]
+
  model = Model(entry_point=entry_point,
  model_data=model_data,
  role='SageMakerRole',
+ dependencies=dependencies,
  framework_version=tf_full_version,
  sagemaker_session=sagemaker_session)
  predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)
@@ -152,12 +156,12 @@ def test_predict_with_entry_point(tfs_predictor_with_model_and_entry_point_same_
  assert expected_result == result
 
 
-def test_predict_with_model_and_entry_point_separated(
-tfs_predictor_with_model_and_entry_point_separated):
+def test_predict_with_model_and_entry_point_and_dependencies_separated(
+tfs_predictor_with_model_and_entry_point_and_dependencies):
  input_data = {'instances': [1.0, 2.0, 5.0]}
  expected_result = {'predictions': [4.0, 4.5, 6.0]}
 
- result = tfs_predictor_with_model_and_entry_point_separated.predict(input_data)
+ result = tfs_predictor_with_model_and_entry_point_and_dependencies.predict(input_data)
  assert expected_result == result