XGBoost Classes for Open Source Version

The Amazon SageMaker XGBoost open source framework algorithm.

class sagemaker.xgboost.estimator.XGBoost(entry_point, framework_version, source_dir=None, hyperparameters=None, py_version='py3', image_uri=None, image_uri_region=None, **kwargs)

Handle end-to-end training and deployment of XGBoost booster training.

It can also handle training using customer provided XGBoost entry point script.

An estimator that 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 fit() on this Estimator. After training is complete, calling deploy() creates a hosted SageMaker endpoint and returns an 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

Parameters
  • entry_point (str or PipelineVariable) – 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 or PipelineVariable) – 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[str, str] or dict[str, PipelineVariable]) – 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 or PipelineVariable) –

    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. .. rubric:: Examples

    123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0 custom-image:latest.

  • image_uri_region (str) – If image_uri argument is None, the image uri associated with this object will be in this region. Default: region associated with SageMaker session.

  • **kwargs – Additional kwargs passed to the Framework constructor.

Tip

You can find additional parameters for initializing this class at Framework and EstimatorBase.

create_model(model_server_workers=None, role=None, vpc_config_override='VPC_CONFIG_DEFAULT', entry_point=None, source_dir=None, dependencies=None, **kwargs)

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

Parameters
  • 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.

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

  • 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.

  • **kwargs – Additional kwargs passed to the XGBoostModel constructor.

Returns

A SageMaker XGBoostModel object.

See XGBoostModel() for full details.

Return type

sagemaker.xgboost.model.XGBoostModel

classmethod attach(training_job_name, sagemaker_session=None, model_channel_name='model')

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()
Parameters
  • 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.

class sagemaker.xgboost.model.XGBoostModel(model_data, role=None, entry_point=None, framework_version=None, image_uri=None, py_version='py3', predictor_cls=<class 'sagemaker.xgboost.model.XGBoostPredictor'>, model_server_workers=None, **kwargs)

Bases: FrameworkModel

An XGBoost SageMaker Model that can be deployed to a SageMaker Endpoint.

Initialize an XGBoostModel.

Parameters
  • model_data (str or PipelineVariable) – The S3 location of a SageMaker model data .tar.gz file.

  • role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.

  • entry_point (str) – Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. If source_dir is specified, then entry_point must point to a file located at the root of source_dir.

  • image_uri (str or PipelineVariable) – A Docker image URI (default: None). If not specified, a default image for XGBoost is be used.

  • py_version (str) – Python version you want to use for executing your model training code (default: ‘py3’).

  • framework_version (str) – XGBoost version you want to use for executing your model training code.

  • predictor_cls (callable[str, sagemaker.session.Session]) – A function to call to create a predictor with an endpoint name and SageMaker Session. If specified, deploy() returns the result of invoking this function on the created endpoint name.

  • model_server_workers (int or PipelineVariable) – Optional. The number of worker processes used by the inference server. If None, server will use one worker per vCPU.

  • **kwargs – Keyword arguments passed to the superclass FrameworkModel and, subsequently, its superclass Model.

Tip

You can find additional parameters for initializing this class at FrameworkModel and Model.

register(content_types=None, response_types=None, inference_instances=None, transform_instances=None, model_package_name=None, model_package_group_name=None, image_uri=None, model_metrics=None, metadata_properties=None, marketplace_cert=False, approval_status=None, description=None, drift_check_baselines=None, customer_metadata_properties=None, domain=None, sample_payload_url=None, task=None, framework=None, framework_version=None, nearest_model_name=None, data_input_configuration=None, skip_model_validation=None, source_uri=None, model_card=None)

Creates a model package for creating SageMaker models or listing on Marketplace.

Parameters
  • content_types (list[str] or list[PipelineVariable]) – The supported MIME types for the input data.

  • response_types (list[str] or list[PipelineVariable]) – The supported MIME types for the output data.

  • inference_instances (list[str] or list[PipelineVariable]) – A list of the instance types that are used to generate inferences in real-time.

  • transform_instances (list[str] or list[PipelineVariable]) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

  • model_package_name (str or PipelineVariable) – Model Package name, exclusive to model_package_group_name, using model_package_name makes the Model Package un-versioned (default: None).

  • model_package_group_name (str or PipelineVariable) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned (default: None).

  • image_uri (str or PipelineVariable) – Inference image uri for the container. Model class’ self.image will be used if it is None (default: None).

  • model_metrics (ModelMetrics) – ModelMetrics object (default: None).

  • metadata_properties (MetadataProperties) – MetadataProperties (default: None).

  • marketplace_cert (bool) – A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False).

  • approval_status (str or PipelineVariable) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval” (default: “PendingManualApproval”).

  • description (str) – Model Package description (default: None).

  • drift_check_baselines (DriftCheckBaselines) – DriftCheckBaselines object (default: None).

  • customer_metadata_properties (dict[str, str] or dict[str, PipelineVariable]) – A dictionary of key-value paired metadata properties (default: None).

  • domain (str or PipelineVariable) – Domain values can be “COMPUTER_VISION”, “NATURAL_LANGUAGE_PROCESSING”, “MACHINE_LEARNING” (default: None).

  • sample_payload_url (str or PipelineVariable) – The S3 path where the sample payload is stored (default: None).

  • task (str or PipelineVariable) – Task values which are supported by Inference Recommender are “FILL_MASK”, “IMAGE_CLASSIFICATION”, “OBJECT_DETECTION”, “TEXT_GENERATION”, “IMAGE_SEGMENTATION”, “CLASSIFICATION”, “REGRESSION”, “OTHER” (default: None).

  • framework (str or PipelineVariable) – Machine learning framework of the model package container image (default: None).

  • framework_version (str or PipelineVariable) – Framework version of the Model Package Container Image (default: None).

  • nearest_model_name (str or PipelineVariable) – Name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender (default: None).

  • data_input_configuration (str or PipelineVariable) – Input object for the model (default: None).

  • skip_model_validation (str or PipelineVariable) – Indicates if you want to skip model validation. Values can be “All” or “None” (default: None).

  • source_uri (str or PipelineVariable) – The URI of the source for the model package (default: None).

  • model_card (ModeCard or ModelPackageModelCard) – document contains qualitative and quantitative information about a model (default: None).

Returns

A string of SageMaker Model Package ARN.

Return type

str

prepare_container_def(instance_type=None, accelerator_type=None, serverless_inference_config=None, accept_eula=None, model_reference_arn=None)

Return a container definition with framework configuration.

The framework configuration is set in model environment variables.

Parameters
  • instance_type (str) – The EC2 instance type to deploy this Model to.

  • accelerator_type (str) – The Elastic Inference accelerator type to deploy to the

  • is (instance for loading and making inferences to the model. This parameter) – unused because accelerator types are not supported by XGBoostModel.

  • serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig) – Specifies configuration related to serverless endpoint. Instance type is not provided in serverless inference. So this is used to find image URIs.

  • accept_eula (bool) – For models that require a Model Access Config, specify True or False to indicate whether model terms of use have been accepted. The accept_eula value must be explicitly defined as True in order to accept the end-user license agreement (EULA) that some models require. (Default: None).

Returns

A container definition object usable with the CreateModel API.

Return type

dict[str, str]

serving_image_uri(region_name, instance_type, serverless_inference_config=None)

Create a URI for the serving image.

Parameters
  • region_name (str) – AWS region where the image is uploaded.

  • instance_type (str) – SageMaker instance type. Must be a CPU instance type.

  • serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig) – Specifies configuration related to serverless endpoint. Instance type is not provided in serverless inference. So this is used to determine device type.

Returns

The appropriate image URI based on the given parameters.

Return type

str

class sagemaker.xgboost.model.XGBoostPredictor(endpoint_name, sagemaker_session=None, serializer=<sagemaker.base_serializers.LibSVMSerializer object>, deserializer=<sagemaker.base_deserializers.CSVDeserializer object>, component_name=None)

Bases: Predictor

A Predictor for inference against XGBoost Endpoints.

This is able to serialize Python lists, dictionaries, and numpy arrays to xgb.DMatrix for XGBoost inference.

Initialize an XGBoostPredictor.

Parameters
  • endpoint_name (str) – The name of the endpoint to perform inference on.

  • 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.

  • serializer (sagemaker.serializers.BaseSerializer) – Optional. Default serializes input data to LibSVM format

  • deserializer (sagemaker.deserializers.BaseDeserializer) – Optional. Default parses the response from text/csv to a Python list.

  • component_name (str) – Optional. Name of the Amazon SageMaker inference component corresponding to the predictor.

class sagemaker.xgboost.processing.XGBoostProcessor(framework_version, role=None, instance_count=None, instance_type=None, py_version='py3', image_uri=None, command=None, volume_size_in_gb=30, volume_kms_key=None, output_kms_key=None, code_location=None, max_runtime_in_seconds=None, base_job_name=None, sagemaker_session=None, env=None, tags=None, network_config=None)

Bases: FrameworkProcessor

Handles Amazon SageMaker processing tasks for jobs using XGBoost containers.

This processor executes a Python script in an XGBoost execution environment.

Unless image_uri is specified, the XGBoost environment is an Amazon-built Docker container that executes functions defined in the supplied code Python script.

The arguments have the exact same meaning as in FrameworkProcessor.

Tip

You can find additional parameters for initializing this class at FrameworkProcessor.

Parameters
estimator_cls

alias of XGBoost