File size: 10,200 Bytes
476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 | # Copyright 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.
"""The pipeline context for workflow"""
from __future__ import absolute_import
import warnings
import inspect
from functools import wraps
from typing import Dict, Optional
from sagemaker.session import Session, SessionSettings
from sagemaker.local import LocalSession
class _StepArguments:
"""Step arguments entity for `Step`"""
def __init__(self, caller_name: str = None):
"""Create a `_StepArguments`
Args:
caller_name (str): The name of the caller function which is intercepted by the
PipelineSession to get the step arguments.
"""
self.caller_name = caller_name
class _JobStepArguments(_StepArguments):
"""Step arguments entity for job step types
Job step types include: TrainingStep, ProcessingStep, TuningStep, TransformStep
"""
def __init__(self, caller_name: str, args: dict):
"""Create a `_JobStepArguments`
Args:
caller_name (str): The name of the caller function which is intercepted by the
PipelineSession to get the step arguments.
args (dict): The arguments to be used for composing the SageMaker API request.
"""
super(_JobStepArguments, self).__init__(caller_name)
self.args = args
class _ModelStepArguments(_StepArguments):
"""Step arguments entity for `ModelStep`"""
def __init__(self, model):
"""Create a `_ModelStepArguments`
Args:
model (Model or PipelineModel): A `sagemaker.model.Model`
or `sagemaker.pipeline.PipelineModel` instance
"""
super(_ModelStepArguments, self).__init__()
self.model = model
self.create_model_package_request = None
self.create_model_request = None
self.need_runtime_repack = set()
self.runtime_repack_output_prefix = None
class PipelineSession(Session):
"""Managing interactions with SageMaker APIs and AWS services needed under Pipeline Context
This class inherits the SageMaker session, it provides convenient methods
for manipulating entities and resources that Amazon SageMaker uses,
such as training jobs, endpoints, and input datasets in S3. When composing
SageMaker Model-Building Pipeline, PipelineSession is recommended over
regular SageMaker Session
"""
def __init__(
self,
boto_session=None,
sagemaker_client=None,
default_bucket=None,
settings=SessionSettings(),
):
"""Initialize a ``PipelineSession``.
Args:
boto_session (boto3.session.Session): The underlying Boto3 session which AWS service
calls are delegated to (default: None). If not provided, one is created with
default AWS configuration chain.
sagemaker_client (boto3.SageMaker.Client): Client which makes Amazon SageMaker service
calls other than ``InvokeEndpoint`` (default: None). Estimators created using this
``Session`` use this client. If not provided, one will be created using this
instance's ``boto_session``.
default_bucket (str): The default Amazon S3 bucket to be used by this session.
This will be created the next time an Amazon S3 bucket is needed (by calling
:func:`default_bucket`).
If not provided, a default bucket will be created based on the following format:
"sagemaker-{region}-{aws-account-id}".
Example: "sagemaker-my-custom-bucket".
settings (sagemaker.session_settings.SessionSettings): Optional. Set of optional
parameters to apply to the session.
"""
super().__init__(
boto_session=boto_session,
sagemaker_client=sagemaker_client,
default_bucket=default_bucket,
settings=settings,
)
self._context = None
@property
def context(self):
"""Hold contextual information useful to the session"""
return self._context
@context.setter
def context(self, value: Optional[_StepArguments] = None):
self._context = value
def _intercept_create_request(self, request: Dict, create, func_name: str = None):
"""This function intercepts the create job request
Args:
request (dict): the create job request
create (functor): a functor calls the sagemaker client create method
func_name (str): the name of the function needed intercepting
"""
if func_name == self.create_model.__name__:
self.context.create_model_request = request
self.context.caller_name = func_name
elif func_name == self.create_model_package_from_containers.__name__:
self.context.create_model_package_request = request
self.context.caller_name = func_name
else:
self.context = _JobStepArguments(func_name, request)
def init_model_step_arguments(self, model):
"""Create a `_ModelStepArguments` (if not exist) as pipeline context
Args:
model (Model or PipelineModel): A `sagemaker.model.Model`
or `sagemaker.pipeline.PipelineModel` instance
"""
if not isinstance(self._context, _ModelStepArguments):
self._context = _ModelStepArguments(model)
class LocalPipelineSession(LocalSession, PipelineSession):
"""Managing a session that executes Sagemaker pipelines and jobs locally in a pipeline context.
This class inherits from the LocalSession and PipelineSession classes.
When running Sagemaker pipelines locally, this class is preferred over LocalSession.
"""
def __init__(
self, boto_session=None, default_bucket=None, s3_endpoint_url=None, disable_local_code=False
):
"""Initialize a ``LocalPipelineSession``.
Args:
boto_session (boto3.session.Session): The underlying Boto3 session which AWS service
calls are delegated to (default: None). If not provided, one is created with
default AWS configuration chain.
default_bucket (str): The default Amazon S3 bucket to be used by this session.
This will be created the next time an Amazon S3 bucket is needed (by calling
:func:`default_bucket`).
If not provided, a default bucket will be created based on the following format:
"sagemaker-{region}-{aws-account-id}".
Example: "sagemaker-my-custom-bucket".
s3_endpoint_url (str): Override the default endpoint URL for Amazon S3,
if set (default: None).
disable_local_code (bool): Set to True to override the default AWS configuration chain
to disable the `local.local_code` setting, which may not be supported for some SDK
features (default: False).
"""
super().__init__(
boto_session=boto_session,
default_bucket=default_bucket,
s3_endpoint_url=s3_endpoint_url,
disable_local_code=disable_local_code,
)
def runnable_by_pipeline(run_func):
"""A convenient Decorator
This is a decorator designed to annotate, during pipeline session,
the methods that downstream managed to
1. preprocess user inputs, outputs, and configurations
2. generate the create request
3. start the job.
For instance, `Processor.run`, `Estimator.fit`, or `Transformer.transform`.
This decorator will essentially run 1, and capture the request shape from 2,
then instead of starting a new job in 3, it will return request shape from 2
to `sagemaker.workflow.steps.Step`. The request shape will be used to construct
the arguments needed to compose that particular step as part of the pipeline.
The job will be started during pipeline execution.
"""
@wraps(run_func)
def wrapper(*args, **kwargs):
self_instance = args[0]
if isinstance(self_instance.sagemaker_session, PipelineSession):
run_func_params = inspect.signature(run_func).parameters
arg_list = list(args)
override_wait, override_logs = False, False
for i, (arg_name, _) in enumerate(run_func_params.items()):
if i >= len(arg_list):
break
if arg_name == "wait":
override_wait = True
arg_list[i] = False
elif arg_name == "logs":
override_logs = True
arg_list[i] = False
args = tuple(arg_list)
if not override_wait and "wait" in run_func_params.keys():
kwargs["wait"] = False
if not override_logs and "logs" in run_func_params.keys():
kwargs["logs"] = False
warnings.warn(
"Running within a PipelineSession, there will be No Wait, "
"No Logs, and No Job being started.",
UserWarning,
)
if run_func.__name__ in ["register", "create"]:
self_instance.sagemaker_session.init_model_step_arguments(self_instance)
run_func(*args, **kwargs)
context = self_instance.sagemaker_session.context
self_instance.sagemaker_session.context = None
return context
run_func(*args, **kwargs)
return self_instance.sagemaker_session.context
return run_func(*args, **kwargs)
return wrapper
|