def model(sagemaker_session):
return FrameworkModel(
image_uri=IMAGE_URI,
model_data=f"s3://{BUCKET}/model.tar.gz",
role=ROLE,
sagemaker_session=sagemaker_session,
entry_point=f"{DATA_DIR}/dummy_script.py",
name="modelName",
vpc_config={"Subnets": ["abc", "def"], "SecurityGroupIds": ["123", "456"]},
)
def create_model(
self,
role=None,
vpc_config_override=VPC_CONFIG_DEFAULT,
entry_point=None,
source_dir=None,
dependencies=None,
):
"""Create a SageMaker ``RLEstimatorModel`` object that can be deployed
to an Endpoint.
Args:
role (str): The ``ExecutionRoleArn`` IAM Role ARN for the ``Model``,
which is also used during transform jobs. If not specified, the
role from the Estimator will be used.
vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
the model. Default: use subnets and security groups from this Estimator.
* 'Subnets' (list[str]): List of subnet ids.
* 'SecurityGroupIds' (list[str]): List of security group ids.
entry_point (str): Path (absolute or relative) to the Python source
file which should be executed as the entry point for MXNet
hosting. This should be compatible with Python 3.5 (default:
self.entry_point)
source_dir (str): Path (absolute or relative) to a directory with
any other training source code dependencies aside from tne entry
point file (default: self.source_dir). Structure within this
directory are preserved when hosting 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: self.dependencies). The library
folders will be copied to SageMaker in the same folder where the
entry_point is copied. If the ```source_dir``` points to S3,
code will be uploaded and the S3 location will be used instead.
Returns:
sagemaker.model.FrameworkModel: Depending on input parameters returns
one of the following:
* sagemaker.model.FrameworkModel - in case image_name was specified
on the estimator;
* sagemaker.mxnet.MXNetModel - if image_name wasn't specified and
MXNet was used as RL backend;
* sagemaker.tensorflow.serving.Model - if image_name wasn't specified and
TensorFlow was used as RL backend.
Raises:
ValueError: If image_name was not specified and framework enum is not valid.
"""
base_args = dict(
model_data=self.model_data,
role=role or self.role,
image=self.image_name,
name=self._current_job_name,
container_log_level=self.container_log_level,
sagemaker_session=self.sagemaker_session,
vpc_config=self.get_vpc_config(vpc_config_override),
)
if not entry_point and (source_dir or dependencies):
raise AttributeError("Please provide an `entry_point`.")
entry_point = entry_point or self.entry_point
source_dir = source_dir or self._model_source_dir()
dependencies = dependencies or self.dependencies
extended_args = dict(
entry_point=entry_point,
source_dir=source_dir,
code_location=self.code_location,
dependencies=dependencies,
enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
)
extended_args.update(base_args)
if self.image_name:
return FrameworkModel(**extended_args)
if self.toolkit == RLToolkit.RAY.value:
raise NotImplementedError(
"Automatic deployment of Ray models is not currently available."
" Train policy parameters are available in model checkpoints"
" in the TrainingJob output.")
if self.framework == RLFramework.TENSORFLOW.value:
from sagemaker.tensorflow.serving import Model as tfsModel
return tfsModel(framework_version=self.framework_version,
**base_args)
if self.framework == RLFramework.MXNET.value:
return MXNetModel(framework_version=self.framework_version,
py_version=PYTHON_VERSION,
**extended_args)
raise ValueError(
"An unknown RLFramework enum was passed in. framework: {}".format(
self.framework))
