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#
# 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.
"""Scrapper utilities to support repacking of models."""
from __future__ import absolute_import
import os
import shutil
import tarfile
import tempfile
from typing import List, Union, Optional, TYPE_CHECKING
from sagemaker import image_uris
from sagemaker.inputs import TrainingInput
from sagemaker.estimator import EstimatorBase
from sagemaker.sklearn.estimator import SKLearn
from sagemaker.workflow.entities import RequestType
from sagemaker.workflow.properties import Properties
from sagemaker.session import get_create_model_package_request, get_model_package_args
from sagemaker.workflow.steps import (
StepTypeEnum,
TrainingStep,
Step,
ConfigurableRetryStep,
)
from sagemaker.utils import _save_model, download_file_from_url
from sagemaker.workflow.retry import RetryPolicy
if TYPE_CHECKING:
from sagemaker.workflow.step_collections import StepCollection
FRAMEWORK_VERSION = "0.23-1"
INSTANCE_TYPE = "ml.m5.large"
REPACK_SCRIPT = "_repack_model.py"
REPACK_SCRIPT_LAUNCHER = "_repack_script_launcher.sh"
LAUNCH_REPACK_SCRIPT_CMD = """
#!/bin/bash
var_dependencies="${SM_HP_DEPENDENCIES}"
var_inference_script="${SM_HP_INFERENCE_SCRIPT}"
var_model_archive="${SM_HP_MODEL_ARCHIVE}"
var_source_dir="${SM_HP_SOURCE_DIR}"
python _repack_model.py \
--dependencies "${var_dependencies}" \
--inference_script "${var_inference_script}" \
--model_archive "${var_model_archive}" \
--source_dir "${var_source_dir}"
"""
class _RepackModelStep(TrainingStep):
"""Repacks model artifacts with custom inference entry points.
The SDK automatically adds this step to pipelines that have RegisterModelSteps with models
that have a custom entry point.
"""
def __init__(
self,
name: str,
sagemaker_session,
role,
model_data: str,
entry_point: str,
display_name: str = None,
description: str = None,
source_dir: str = None,
dependencies: List = None,
depends_on: Optional[List[Union[str, Step, "StepCollection"]]] = None,
retry_policies: List[RetryPolicy] = None,
subnets=None,
security_group_ids=None,
**kwargs,
):
"""Base class initializer.
Args:
name (str): The name of the training step.
sagemaker_session (sagemaker.session.Session): Session object which manages
interactions with Amazon SageMaker APIs and any other AWS services needed. If
not specified, the estimator creates one using the default
AWS configuration chain.
role (str): An AWS IAM role (either name or full ARN). The Amazon
SageMaker training jobs and APIs that create Amazon SageMaker
endpoints use this role to access training data and model
artifacts. After the endpoint is created, the inference code
might use the IAM role, if it needs to access an AWS resource.
model_data (str): The S3 location of a SageMaker model data `.tar.gz` file.
entry_point (str): Path (absolute or relative) to the local Python
source file which should be executed as the entry point to
inference. If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
If 'git_config' is provided, 'entry_point' should be
a relative location to the Python source file in the Git repo.
Example:
With the following GitHub repo directory structure:
>>> |----- README.md
>>> |----- src
>>> |----- train.py
>>> |----- test.py
You can assign entry_point='src/train.py'.
display_name (str): The display name of this `_RepackModelStep` step (default: None).
description (str): The description of this `_RepackModelStep` (default: None).
source_dir (str): A relative location to a directory with other training
or model hosting source code dependencies aside from the entry point
file in the Git repo (default: None). Structure within this
directory are preserved when training on Amazon SageMaker.
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. If 'git_config' is provided, 'dependencies' should be a
list of relative locations to directories with any additional
libraries needed in the Git repo.
.. admonition:: 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
This is not supported with "local code" in Local Mode.
depends_on (List[Union[str, Step, StepCollection]]): The list of `Step`/`StepCollection`
names or `Step` instances or `StepCollection` instances that the current `Step`
depends on (default: None).
retry_policies (List[RetryPolicy]): The list of retry policies for the current step
(default: None).
subnets (list[str]): List of subnet ids. If not specified, the re-packing
job will be created without VPC config (default: None).
security_group_ids (list[str]): List of security group ids. If not
specified, the re-packing job will be created without VPC config (default: None).
**kwargs: additional arguments for the repacking job.
"""
self._model_data = model_data
self.sagemaker_session = sagemaker_session
self.role = role
self._entry_point = entry_point
self._entry_point_basename = os.path.basename(self._entry_point)
self._source_dir = source_dir
self._dependencies = dependencies
# convert dependencies array into space-delimited string
dependencies_hyperparameter = None
if self._dependencies:
dependencies_hyperparameter = " ".join(self._dependencies)
# the real estimator and inputs
repacker = SKLearn(
framework_version=FRAMEWORK_VERSION,
instance_type=INSTANCE_TYPE,
entry_point=REPACK_SCRIPT_LAUNCHER,
source_dir=self._source_dir,
dependencies=self._dependencies,
sagemaker_session=self.sagemaker_session,
role=self.role,
hyperparameters={
"inference_script": self._entry_point_basename,
"model_archive": self._model_data,
"dependencies": dependencies_hyperparameter,
"source_dir": self._source_dir,
},
subnets=subnets,
security_group_ids=security_group_ids,
**kwargs,
)
repacker.disable_profiler = True
inputs = TrainingInput(self._model_data)
# super!
super(_RepackModelStep, self).__init__(
name=name,
display_name=display_name,
description=description,
depends_on=depends_on,
retry_policies=retry_policies,
estimator=repacker,
inputs=inputs,
)
def _prepare_for_repacking(self):
"""Prepares the source for the estimator."""
if self._source_dir is None:
self._establish_source_dir()
self._inject_repack_script_and_launcher()
def _establish_source_dir(self):
"""If the source_dir is None, creates it for the repacking job.
It performs the following:
1) creates a source directory
2) copies the inference_entry_point inside it
3) copies the repack_model.py inside it
4) sets the source dir for the repacking estimator
"""
self._source_dir = tempfile.mkdtemp()
self.estimator.source_dir = self._source_dir
shutil.copy2(self._entry_point, os.path.join(self._source_dir, self._entry_point_basename))
self._entry_point = self._entry_point_basename
def _inject_repack_script_and_launcher(self):
"""Injects the _repack_model.py script and _repack_script_launcher.sh
into S3 or local source directory.
Note: The bash file is needed because if not supplied, the SKLearn
training job will auto install all dependencies listed in requirements.txt.
However, this auto install behavior is not expected in _RepackModelStep,
since it should just simply repack the model along with other supplied files,
e.g. the requirements.txt.
If the source_dir is an S3 path:
1) downloads the source_dir tar.gz
2) extracts it
3) copies the _repack_model.py script into the extracted directory
4) creates the _repack_script_launcher.sh in the extracted dir
5) rezips the directory
6) overwrites the S3 source_dir with the new tar.gz
If the source_dir is a local path:
1) copies the _repack_model.py script into the source dir
2) creates the _repack_script_launcher.sh in the source dir
"""
fname = os.path.join(os.path.dirname(__file__), REPACK_SCRIPT)
if self._source_dir.lower().startswith("s3://"):
with tempfile.TemporaryDirectory() as tmp:
targz_contents_dir = os.path.join(tmp, "extracted")
old_targz_path = os.path.join(tmp, "old.tar.gz")
download_file_from_url(self._source_dir, old_targz_path, self.sagemaker_session)
with tarfile.open(name=old_targz_path, mode="r:gz") as t:
t.extractall(path=targz_contents_dir)
shutil.copy2(fname, os.path.join(targz_contents_dir, REPACK_SCRIPT))
with open(
os.path.join(targz_contents_dir, REPACK_SCRIPT_LAUNCHER), "w"
) as launcher_file:
launcher_file.write(LAUNCH_REPACK_SCRIPT_CMD)
new_targz_path = os.path.join(tmp, "new.tar.gz")
with tarfile.open(new_targz_path, mode="w:gz") as t:
t.add(targz_contents_dir, arcname=os.path.sep)
_save_model(self._source_dir, new_targz_path, self.sagemaker_session, kms_key=None)
else:
shutil.copy2(fname, os.path.join(self._source_dir, REPACK_SCRIPT))
with open(os.path.join(self._source_dir, REPACK_SCRIPT_LAUNCHER), "w") as launcher_file:
launcher_file.write(LAUNCH_REPACK_SCRIPT_CMD)
@property
def arguments(self) -> RequestType:
"""The arguments dict that are used to call `create_training_job`.
This first prepares the source bundle for repackinglby placing artifacts
in locations which the training container will make available to the
repacking script and then gets the arguments for the training job.
"""
self._prepare_for_repacking()
return super(_RepackModelStep, self).arguments
@property
def properties(self):
"""A Properties object representing the DescribeTrainingJobResponse data model."""
return self._properties
class _RegisterModelStep(ConfigurableRetryStep):
"""Register model step in workflow that creates a model package."""
def __init__(
self,
name: str,
step_args: Optional[dict] = None,
content_types: Optional[list] = None,
response_types: Optional[list] = None,
inference_instances: Optional[list] = None,
transform_instances: Optional[list] = None,
estimator: EstimatorBase = None,
model_data=None,
model_package_group_name=None,
model_metrics=None,
metadata_properties=None,
approval_status="PendingManualApproval",
image_uri=None,
compile_model_family=None,
display_name: str = None,
description=None,
depends_on: Optional[List[Union[str, Step, "StepCollection"]]] = None,
retry_policies: Optional[List[RetryPolicy]] = None,
tags=None,
container_def_list=None,
drift_check_baselines=None,
customer_metadata_properties=None,
domain=None,
sample_payload_url=None,
task=None,
**kwargs,
):
"""Constructor of a register model step.
Args:
name (str): The name of the training step.
step_args (dict): The arguments for this `_RegisterModelStep` definition
(default: None).
content_types (list): The supported MIME types for the input data (default: None).
response_types (list): The supported MIME types for the output data (default: None).
inference_instances (list): A list of the instance types that are used to
generate inferences in real-time (default: None).
transform_instances (list): A list of the instance types on which a
transformation job can be run or on which an endpoint
can be deployed (default: None).
estimator (EstimatorBase): A `sagemaker.estimator.EstimatorBase` instance
(default: None).
model_data: the S3 URI to the model data from training (default: None).
model_package_group_name (str): Model Package Group name, exclusive to
`model_package_name`, using `model_package_group_name`
makes the Model Package versioned (default: None).
model_metrics (ModelMetrics): ModelMetrics object (default: None).
metadata_properties (MetadataProperties): MetadataProperties object (default: None).
approval_status (str): Model Approval Status, values can be "Approved",
"Rejected", or "PendingManualApproval" (default: "PendingManualApproval").
image_uri (str): The container image uri for Model Package, if not specified,
Estimator's training container image will be used (default: None).
compile_model_family (str): Instance family for compiled model,
if specified, a compiled model will be used (default: None).
display_name (str): The display name of this `_RegisterModelStep` step (default: None).
description (str): Model Package description (default: None).
depends_on (List[Union[str, Step, StepCollection]]): The list of `Step`/`StepCollection`
names or `Step` instances or `StepCollection` instances that the current `Step`
depends on (default: None).
retry_policies (List[RetryPolicy]): The list of retry policies for the current step
(default: None).
tags (List[dict[str, str]]): A list of dictionaries containing key-value pairs used to
configure the create model package request (default: None).
container_def_list (list): A list of container definitions (default: None).
drift_check_baselines (DriftCheckBaselines): DriftCheckBaselines object (default: None).
customer_metadata_properties (dict[str, str]): A dictionary of key-value paired
metadata properties (default: None).
domain (str): Domain values can be "COMPUTER_VISION", "NATURAL_LANGUAGE_PROCESSING",
"MACHINE_LEARNING" (default: None).
sample_payload_url (str): The S3 path where the sample payload is stored
(default: None).
task (str): Task values which are supported by Inference Recommender are "FILL_MASK",
"IMAGE_CLASSIFICATION", "OBJECT_DETECTION", "TEXT_GENERATION", "IMAGE_SEGMENTATION",
"CLASSIFICATION", "REGRESSION", "OTHER" (default: None).
**kwargs: additional arguments to `create_model`.
"""
super(_RegisterModelStep, self).__init__(
name, StepTypeEnum.REGISTER_MODEL, display_name, description, depends_on, retry_policies
)
deprecated_args_missing = content_types is None or response_types is None
if not (step_args is None) ^ deprecated_args_missing:
raise ValueError(
"step_args and the set of (content_types, response_types, "
") are mutually exclusive. "
"Either of them should be provided."
)
self.step_args = step_args
self.estimator = estimator
self.model_data = model_data
self.content_types = content_types
self.response_types = response_types
self.inference_instances = inference_instances
self.transform_instances = transform_instances
self.model_package_group_name = model_package_group_name
self.tags = tags
self.model_metrics = model_metrics
self.drift_check_baselines = drift_check_baselines
self.customer_metadata_properties = customer_metadata_properties
self.domain = domain
self.sample_payload_url = sample_payload_url
self.task = task
self.metadata_properties = metadata_properties
self.approval_status = approval_status
self.image_uri = image_uri
self.compile_model_family = compile_model_family
self.description = description
self.tags = tags
self.kwargs = kwargs
self.container_def_list = container_def_list
self._properties = Properties(step_name=name, shape_name="DescribeModelPackageOutput")
@property
def arguments(self) -> RequestType:
"""The arguments dict that are used to call `create_model_package`."""
model_name = self.name
if self.step_args:
request_dict = self.step_args
else:
if self.container_def_list is None:
if self.compile_model_family:
model = self.estimator._compiled_models[self.compile_model_family]
self.model_data = model.model_data
else:
# create_model wants the estimator to have a model_data attribute...
self.estimator._current_job_name = model_name
# placeholder. replaced with model_data later
output_path = self.estimator.output_path
self.estimator.output_path = "/tmp"
# create the model, but custom funky framework stuff going on in some places
if self.image_uri:
model = self.estimator.create_model(image_uri=self.image_uri, **self.kwargs)
else:
model = self.estimator.create_model(**self.kwargs)
self.image_uri = model.image_uri
if self.model_data is None:
self.model_data = model.model_data
# reset placeholder
self.estimator.output_path = output_path
# yeah, there is some framework stuff going on that we need to pull in here
if self.image_uri is None:
region_name = self.estimator.sagemaker_session.boto_session.region_name
self.image_uri = image_uris.retrieve(
model._framework_name,
region_name,
version=model.framework_version,
py_version=model.py_version if hasattr(model, "py_version") else None,
instance_type=self.kwargs.get(
"instance_type", self.estimator.instance_type
),
accelerator_type=self.kwargs.get("accelerator_type"),
image_scope="inference",
)
model.name = model_name
model.model_data = self.model_data
model_package_args = get_model_package_args(
content_types=self.content_types,
response_types=self.response_types,
inference_instances=self.inference_instances,
transform_instances=self.transform_instances,
model_package_group_name=self.model_package_group_name,
model_data=self.model_data,
image_uri=self.image_uri,
model_metrics=self.model_metrics,
drift_check_baselines=self.drift_check_baselines,
metadata_properties=self.metadata_properties,
approval_status=self.approval_status,
description=self.description,
tags=self.tags,
container_def_list=self.container_def_list,
customer_metadata_properties=self.customer_metadata_properties,
domain=self.domain,
sample_payload_url=self.sample_payload_url,
task=self.task,
)
request_dict = get_create_model_package_request(**model_package_args)
# these are not available in the workflow service and will cause rejection
if "CertifyForMarketplace" in request_dict:
request_dict.pop("CertifyForMarketplace")
if "Description" in request_dict:
request_dict.pop("Description")
return request_dict
@property
def properties(self):
"""A Properties object representing the DescribeModelPackageOutput data model."""
return self._properties
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