SKLearn: Scikit-learn Class

In DyfanJones/sagemaker-r-mlframework: sagemaker machine learning developed by amazon

Scikit-learn Class

Description

Handle end-to-end training and deployment of custom Scikit-learn code.

Super classes

sagemaker.mlcore::EstimatorBase -> sagemaker.mlcore::Framework -> SKLearn

Public fields

.module

mimic python module

Methods

Public methods

• SKLearn$new()

• SKLearn$create_model()

• SKLearn$clone()

Inherited methods

• sagemaker.mlcore::EstimatorBase$compile_model()

• sagemaker.mlcore::EstimatorBase$delete_endpoint()

• sagemaker.mlcore::EstimatorBase$deploy()

• sagemaker.mlcore::EstimatorBase$describe()

• sagemaker.mlcore::EstimatorBase$disable_profiling()

• sagemaker.mlcore::EstimatorBase$enable_default_profiling()

• sagemaker.mlcore::EstimatorBase$enable_network_isolation()

• sagemaker.mlcore::EstimatorBase$fit()

• sagemaker.mlcore::EstimatorBase$format()

• sagemaker.mlcore::EstimatorBase$get_vpc_config()

• sagemaker.mlcore::EstimatorBase$help()

• sagemaker.mlcore::EstimatorBase$latest_job_debugger_artifacts_path()

• sagemaker.mlcore::EstimatorBase$latest_job_profiler_artifacts_path()

• sagemaker.mlcore::EstimatorBase$latest_job_tensorboard_artifacts_path()

• sagemaker.mlcore::EstimatorBase$logs()

• sagemaker.mlcore::EstimatorBase$prepare_workflow_for_training()

• sagemaker.mlcore::EstimatorBase$register()

• sagemaker.mlcore::EstimatorBase$rule_job_summary()

• sagemaker.mlcore::EstimatorBase$update_profiler()

• sagemaker.mlcore::EstimatorBase$wait()

• sagemaker.mlcore::Framework$.prepare_for_training()

• sagemaker.mlcore::Framework$attach()

• sagemaker.mlcore::Framework$hyperparameters()

• sagemaker.mlcore::Framework$training_image_uri()

• sagemaker.mlcore::Framework$transformer()

---

Method new()

This “Estimator“ executes an Scikit-learn script in a managed Scikit-learn execution environment, within a SageMaker Training Job. The managed Scikit-learn 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 returns an :class:'~sagemaker.amazon.sklearn.model.SKLearnPredictor' instance that can be used to perform inference against the hosted model. Technical documentation on preparing Scikit-learn scripts for SageMaker training and using the Scikit-learn Estimator is available on the project home-page: https://github.com/aws/sagemaker-python-sdk

Usage

SKLearn$new(
  entry_point,
  framework_version = NULL,
  py_version = "py3",
  source_dir = NULL,
  hyperparameters = NULL,
  image_uri = 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“.

framework_version

(str): Scikit-learn version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators

py_version

(str): Python version you want to use for executing your model training code (default: 'py3'). Currently, 'py3' is the only supported version. If “None“ is passed in, “image_uri“ must be provided.

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: None). 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: None). 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, but “str()“ will be called to convert them before training.

image_uri

(str): 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. It can be an ECR url or dockerhub image and tag. Examples: 123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0 custom-image:latest. If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

...

: Additional kwargs passed to the :class:'~sagemaker.estimator.Framework' constructor.

---

Method create_model()

Create a SageMaker “SKLearnModel“ object that can be deployed to an “Endpoint“.

Usage

SKLearn$create_model(
  model_server_workers = NULL,
  role = NULL,
  vpc_config_override = "VPC_CONFIG_DEFAULT",
  entry_point = NULL,
  source_dir = NULL,
  dependencies = NULL,
  ...
)

Arguments

model_server_workers

(int): Optional. The number of worker processes used by the inference server. If None, server will use one worker per vCPU.

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 local 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“. If not specified, the training entry point is used.

source_dir

(str): Path (absolute or relative) to a directory with any other serving source code dependencies aside from the entry point file. If not specified, the model source directory from training is used.

dependencies

(list[str]): A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container. If not specified, the dependencies from training are used. This is not supported with "local code" in Local Mode.

...

: Additional kwargs passed to the :class:'~sagemaker.sklearn.model.SKLearnModel' constructor.

Returns

sagemaker.sklearn.model.SKLearnModel: A SageMaker “SKLearnModel“ object. See :func:'~sagemaker.sklearn.model.SKLearnModel' for full details.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

SKLearn$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-mlframework documentation built on March 18, 2022, 7:41 a.m.