RLEstimator: RLEstimator Class
In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon
RLEstimator R Documentation
RLEstimator Class
Description
Handle end-to-end training and deployment of custom RLEstimator code.
Super classes
sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::Framework -> RLEstimator
Public fields
COACH_LATEST_VERSION_TFlatest version of toolkit coach for tensorflow
COACH_LATEST_VERSION_MXNETlatest version of toolkit coach for mxnet
RAY_LATEST_VERSIONlatest version of toolkit ray
.modulemimic python module
Methods
Public methods
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Inherited methods
Method new()
Creates an RLEstimator for managed Reinforcement Learning (RL).
It will execute an RLEstimator script within a SageMaker Training Job. The managed RL
environment is an Amazon-built Docker container that executes functions defined in the
supplied “entry_point“ Python script.
Training is started by calling
:meth:'~sagemaker.amazon.estimator.Framework.fit' on this Estimator.
After training is complete, calling
:meth:'~sagemaker.amazon.estimator.Framework.deploy' creates a hosted
SageMaker endpoint and based on the specified framework returns an
:class:'~sagemaker.amazon.mxnet.model.MXNetPredictor' or
:class:'~sagemaker.amazon.tensorflow.model.TensorFlowPredictor' instance that
can be used to perform inference against the hosted model.
Technical documentation on preparing RLEstimator scripts for
SageMaker training and using the RLEstimator is available on the project
homepage: https://github.com/aws/sagemaker-python-sdk
Usage
RLEstimator$new(
entry_point,
toolkit = NULL,
toolkit_version = NULL,
framework = NULL,
source_dir = NULL,
hyperparameters = NULL,
image_uri = NULL,
metric_definitions = NULL,
...
)
Arguments
entry_point(str): Path (absolute or relative) to the Python source
file which should be executed as the entry point to training.
If “source_dir“ is specified, then “entry_point“
must point to a file located at the root of “source_dir“.
toolkit(sagemaker.rl.RLToolkit): RL toolkit you want to use for
executing your model training code.
toolkit_version(str): RL toolkit version you want to be use for
executing your model training code.
framework(sagemaker.rl.RLFramework): Framework (MXNet or
TensorFlow) you want to be used as a toolkit backed for
reinforcement learning training.
source_dir(str): Path (absolute, relative or an S3 URI) to a directory
with any other training source code dependencies aside from the entry
point file (default: NULL). If “source_dir“ is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters(dict): Hyperparameters that will be used for
training (default: NULL). The hyperparameters are made
accessible as a dict[str, str] to the training code on
SageMaker. For convenience, this accepts other types for keys
and values.
image_uri(str): An ECR url. If specified, the estimator will use
this image for training and hosting, instead of selecting the
appropriate SageMaker official image based on framework_version
and py_version. Example:
123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
metric_definitions(list[dict]): A list of dictionaries that defines
the metric(s) used to evaluate the training jobs. Each
dictionary contains two keys: 'Name' for the name of the metric,
and 'Regex' for the regular expression used to extract the
metric from the logs. This should be defined only for jobs that
don't use an Amazon algorithm.
...: Additional kwargs passed to the
:class:'~sagemaker.estimator.Framework' constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:'~sagemaker.estimator.Framework' and
:class:'~sagemaker.estimator.EstimatorBase'.
Method create_model()
Create a SageMaker “RLEstimatorModel“ object that can be deployed to an Endpoint.
Usage
RLEstimator$create_model(
role = NULL,
vpc_config_override = "VPC_CONFIG_DEFAULT",
entry_point = NULL,
source_dir = NULL,
dependencies = NULL,
...
)
Arguments
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 (default: self.entry_point). If “source_dir“ is specified,
then “entry_point“ must point to a file located at the root of “source_dir“.
source_dir(str): Path (absolute or relative) to a directory with
any other training source code dependencies aside from the 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.
This is not supported with "local code" in Local Mode.
...: Additional kwargs passed to the :class:'~sagemaker.model.FrameworkModel'
constructor.
Returns
sagemaker.model.FrameworkModel: Depending on input parameters returns
one of the following:
* :class:'~sagemaker.model.FrameworkModel' - if “image_uri“ is specified
on the estimator;
* :class:‘~sagemaker.mxnet.MXNetModel' - if “image_uri“ isn’t specified and
MXNet is used as the RL backend;
* :class:‘~sagemaker.tensorflow.model.TensorFlowModel' - if “image_uri“ isn’t
specified and TensorFlow is used as the RL backend.
Method training_image_uri()
Return the Docker image to use for training.
The :meth:'~sagemaker.estimator.EstimatorBase.fit' method, which does
the model training, calls this method to find the image to use for model
training.
Usage
RLEstimator$training_image_uri()
Returns
str: The URI of the Docker image.
Method hyperparameters()
Return hyperparameters used by your custom TensorFlow code during model training.
Usage
RLEstimator$hyperparameters()
Method default_metric_definitions()
Provides default metric definitions based on provided toolkit.
Usage
RLEstimator$default_metric_definitions(toolkit)
Arguments
toolkit(sagemaker.rl.RLToolkit): RL Toolkit to be used for
training.
Returns
list: metric definitions
Method clone()
The objects of this class are cloneable with this method.
Usage
RLEstimator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.
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| RLEstimator | R Documentation |
RLEstimator Class
Description
Handle end-to-end training and deployment of custom RLEstimator code.
Super classes
sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::Framework -> RLEstimator
Public fields
COACH_LATEST_VERSION_TFlatest version of toolkit coach for tensorflow
COACH_LATEST_VERSION_MXNETlatest version of toolkit coach for mxnet
RAY_LATEST_VERSIONlatest version of toolkit ray
.modulemimic python module
Methods
Public methods
Inherited methods
Method new()
Creates an RLEstimator for managed Reinforcement Learning (RL). It will execute an RLEstimator script within a SageMaker Training Job. The managed RL environment is an Amazon-built Docker container that executes functions defined in the supplied “entry_point“ Python script. Training is started by calling :meth:'~sagemaker.amazon.estimator.Framework.fit' on this Estimator. After training is complete, calling :meth:'~sagemaker.amazon.estimator.Framework.deploy' creates a hosted SageMaker endpoint and based on the specified framework returns an :class:'~sagemaker.amazon.mxnet.model.MXNetPredictor' or :class:'~sagemaker.amazon.tensorflow.model.TensorFlowPredictor' instance that can be used to perform inference against the hosted model. Technical documentation on preparing RLEstimator scripts for SageMaker training and using the RLEstimator is available on the project homepage: https://github.com/aws/sagemaker-python-sdk
Usage
RLEstimator$new( entry_point, toolkit = NULL, toolkit_version = NULL, framework = NULL, source_dir = NULL, hyperparameters = NULL, image_uri = NULL, metric_definitions = NULL, ... )
Arguments
entry_point(str): Path (absolute or relative) to the Python source file which should be executed as the entry point to training. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“.
toolkit(sagemaker.rl.RLToolkit): RL toolkit you want to use for executing your model training code.
toolkit_version(str): RL toolkit version you want to be use for executing your model training code.
framework(sagemaker.rl.RLFramework): Framework (MXNet or TensorFlow) you want to be used as a toolkit backed for reinforcement learning training.
source_dir(str): Path (absolute, relative or an S3 URI) to a directory with any other training source code dependencies aside from the entry point file (default: NULL). If “source_dir“ is an S3 URI, it must point to a tar.gz file. Structure within this directory are preserved when training on Amazon SageMaker.
hyperparameters(dict): Hyperparameters that will be used for training (default: NULL). The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker. For convenience, this accepts other types for keys and values.
image_uri(str): An ECR url. If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. Example: 123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
metric_definitions(list[dict]): A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for the regular expression used to extract the metric from the logs. This should be defined only for jobs that don't use an Amazon algorithm.
...: Additional kwargs passed to the :class:'~sagemaker.estimator.Framework' constructor. .. tip:: You can find additional parameters for initializing this class at :class:'~sagemaker.estimator.Framework' and :class:'~sagemaker.estimator.EstimatorBase'.
Method create_model()
Create a SageMaker “RLEstimatorModel“ object that can be deployed to an Endpoint.
Usage
RLEstimator$create_model( role = NULL, vpc_config_override = "VPC_CONFIG_DEFAULT", entry_point = NULL, source_dir = NULL, dependencies = NULL, ... )
Arguments
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 (default: self.entry_point). If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“.
source_dir(str): Path (absolute or relative) to a directory with any other training source code dependencies aside from the 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. This is not supported with "local code" in Local Mode.
...: Additional kwargs passed to the :class:'~sagemaker.model.FrameworkModel' constructor.
Returns
sagemaker.model.FrameworkModel: Depending on input parameters returns one of the following: * :class:'~sagemaker.model.FrameworkModel' - if “image_uri“ is specified on the estimator; * :class:‘~sagemaker.mxnet.MXNetModel' - if “image_uri“ isn’t specified and MXNet is used as the RL backend; * :class:‘~sagemaker.tensorflow.model.TensorFlowModel' - if “image_uri“ isn’t specified and TensorFlow is used as the RL backend.
Method training_image_uri()
Return the Docker image to use for training. The :meth:'~sagemaker.estimator.EstimatorBase.fit' method, which does the model training, calls this method to find the image to use for model training.
Usage
RLEstimator$training_image_uri()
Returns
str: The URI of the Docker image.
Method hyperparameters()
Return hyperparameters used by your custom TensorFlow code during model training.
Usage
RLEstimator$hyperparameters()
Method default_metric_definitions()
Provides default metric definitions based on provided toolkit.
Usage
RLEstimator$default_metric_definitions(toolkit)
Arguments
toolkit(sagemaker.rl.RLToolkit): RL Toolkit to be used for training.
Returns
list: metric definitions
Method clone()
The objects of this class are cloneable with this method.
Usage
RLEstimator$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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