XGBoost: XGBoost Class

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

XGBoost Class

Description

Handle end-to-end training and deployment of XGBoost booster training or training using customer provided XGBoost entry point script.

Super classes

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

Public fields

.module

mimic python module

Methods

Public methods

• XGBoost$new()

• XGBoost$create_model()

• XGBoost$attach()

• XGBoost$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$hyperparameters()

• sagemaker.mlcore::Framework$training_image_uri()

• sagemaker.mlcore::Framework$transformer()

---

Method new()

This “Estimator“ executes an XGBoost based SageMaker Training Job. The managed XGBoost environment is an Amazon-built Docker container thatexecutes 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.xgboost.model.XGBoostPredictor' instance that can be used to perform inference against the hosted model. Technical documentation on preparing XGBoost scripts for SageMaker training and using the XGBoost Estimator is available on the project home-page: https://github.com/aws/sagemaker-python-sdk

Usage

XGBoost$new(
  entry_point,
  framework_version,
  source_dir = NULL,
  hyperparameters = NULL,
  py_version = "py3",
  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): XGBoost version you want to use for executing your model training code.

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.

py_version

(str): Python version you want to use for executing your model training code (default: 'py3').

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.

...

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

---

Method create_model()

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

Usage

XGBoost$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.xgboost.model.XGBoostModel' constructor.

Returns

sagemaker.xgboost.model.XGBoostModel: A SageMaker “XGBoostModel“ object. See :func:'~sagemaker.xgboost.model.XGBoostModel' for full details.

---

Method attach()

Attach to an existing training job. Create an Estimator bound to an existing training job, each subclass is responsible to implement “_prepare_init_params_from_job_description()“ as this method delegates the actual conversion of a training job description to the arguments that the class constructor expects. After attaching, if the training job has a Complete status, it can be “deploy()“ ed to create a SageMaker Endpoint and return a “Predictor“. If the training job is in progress, attach will block and display log messages from the training job, until the training job completes. Examples: >>> my_estimator.fit(wait=False) >>> training_job_name = my_estimator.latest_training_job.name Later on: >>> attached_estimator = Estimator.attach(training_job_name) >>> attached_estimator.deploy()

Usage

XGBoost$attach(
  training_job_name,
  sagemaker_session = NULL,
  model_channel_name = "model"
)

Arguments

training_job_name

(str): The name of the training job to attach to.

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.

model_channel_name

(str): Name of the channel where pre-trained model data will be downloaded (default: 'model'). If no channel with the same name exists in the training job, this option will be ignored.

Returns

Instance of the calling “Estimator“ Class with the attached training job.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

XGBoost$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.