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, callingdeploy()
creates a hosted SageMaker endpoint and returns anXGBoostPredictor
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, thenentry_point
must point to a file located at the root ofsource_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
andEstimatorBase
.- 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 anEndpoint
.- Parameters
role (str) – The
ExecutionRoleArn
IAM Role ARN for theModel
, 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, thenentry_point
must point to a file located at the root ofsource_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.
- A SageMaker
- Return type
- 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 bedeploy()
ed to create a SageMaker Endpoint and return aPredictor
.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 SageMakerEndpoint
.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, thenentry_point
must point to a file located at the root ofsource_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 superclassModel
.
Tip
You can find additional parameters for initializing this class at
FrameworkModel
andModel
.- 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
- 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
- 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
- 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 suppliedcode
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
framework_version (str) –
role (str) –
instance_count (Union[int, PipelineVariable]) –
instance_type (Union[str, PipelineVariable]) –
py_version (str) –
image_uri (Optional[Union[str, PipelineVariable]]) –
volume_size_in_gb (Union[int, PipelineVariable]) –
volume_kms_key (Optional[Union[str, PipelineVariable]]) –
output_kms_key (Optional[Union[str, PipelineVariable]]) –
max_runtime_in_seconds (Optional[Union[int, PipelineVariable]]) –
tags (Optional[Union[List[Dict[str, Union[str, PipelineVariable]]], Dict[str, Union[str, PipelineVariable]]]]) –
network_config (Optional[NetworkConfig]) –