Scikit Learn — sagemaker 2.199.0 documentation (original) (raw)

sagemaker

Scikit Learn Estimator¶

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

Bases: sagemaker.estimator.Framework

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

Creates a SKLearn Estimator for Scikit-learn environment.

It will execute an Scikit-learn script 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 callingfit() on this Estimator. After training is complete, callingdeploy() creates a hosted SageMaker endpoint and returns anSKLearnPredictor 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

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_pointmust 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://docs.aws.amazon.com/sagemaker/latest/dg/sklearn.html
• 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 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.
• 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.
  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, thenimage_uri is required. If also None, then a ValueErrorwill be raised.
• 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 theFramework constructor.

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 SKLearnModel object that can be deployed to an Endpoint.

Parameters

• 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.
• **kwargs – Additional kwargs passed to the SKLearnModelconstructor.

Returns

A SageMaker SKLearnModelobject. See SKLearnModel() for full details.

Return type

sagemaker.sklearn.model.SKLearnModel

Scikit Learn Model¶

class sagemaker.sklearn.model. SKLearnModel(model_data, role=None, entry_point=None, framework_version=None, py_version='py3', image_uri=None, predictor_cls=<class 'sagemaker.sklearn.model.SKLearnPredictor'>, model_server_workers=None, **kwargs)¶

Bases: sagemaker.model.FrameworkModel

An Scikit-learn SageMaker Model that can be deployed to a SageMaker Endpoint.

Initialize an SKLearnModel.

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_pointmust 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.
• 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.
• image_uri (str or PipelineVariable) – A Docker image URI (default: None). If not specified, a default image for Scikit-learn will be used. If framework_version or py_version are None, thenimage_uri is required. If image_uri is also None, then a ValueErrorwill be raised.
• 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 FrameworkModelinitializer.

Tip

You can find additional parameters for initializing this class atFrameworkModel 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)¶

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 (default: None).
• 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 (default: None).
• model_package_name (str or PipelineVariable) – Model Package name, exclusive tomodel_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 object (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).

Returns

A sagemaker.model.ModelPackage instance.

prepare_container_def(instance_type=None, accelerator_type=None, serverless_inference_config=None)¶

Container definition with framework configuration set in model environment variables.

Parameters

• instance_type (str) – The EC2 instance type to deploy this Model to. This parameter is unused because Scikit-learn supports only CPU.
• accelerator_type (str) – The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. This parameter is unused because accelerator types are not supported by SKLearnModel.
• 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.

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.
• 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

Scikit Learn Predictor¶

class sagemaker.sklearn.model. SKLearnPredictor(endpoint_name, sagemaker_session=None, serializer=<sagemaker.base_serializers.NumpySerializer object>, deserializer=<sagemaker.base_deserializers.NumpyDeserializer object>, component_name=None)¶

Bases: sagemaker.base_predictor.Predictor

A Predictor for inference against Scikit-learn Endpoints.

This is able to serialize Python lists, dictionaries, and numpy arrays to multidimensional tensors for Scikit-learn inference.

Initialize an SKLearnPredictor.

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 .npy format. Handles lists and numpy arrays.
• deserializer (sagemaker.deserializers.BaseDeserializer) – Optional. Default parses the response from .npy format to numpy array.
• component_name (str) – Optional. Name of the Amazon SageMaker inference component corresponding to the predictor.

Scikit Learn Processor¶

class sagemaker.sklearn.processing. SKLearnProcessor(framework_version, role=None, instance_count=None, instance_type=None, command=None, volume_size_in_gb=30, volume_kms_key=None, output_kms_key=None, max_runtime_in_seconds=None, base_job_name=None, sagemaker_session=None, env=None, tags=None, network_config=None)¶

Bases: sagemaker.processing.ScriptProcessor

Handles Amazon SageMaker processing tasks for jobs using scikit-learn.

Initialize an SKLearnProcessor instance.

The SKLearnProcessor handles Amazon SageMaker processing tasks for jobs using scikit-learn.

Parameters

• framework_version (str) – The version of scikit-learn.
• role (str) – An AWS IAM role name or 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.
• instance_type (str or PipelineVariable) – Type of EC2 instance to use for processing, for example, ‘ml.c4.xlarge’.
• instance_count (int or PipelineVariable) – The number of instances to run the Processing job with. Defaults to 1.
• command (_[_str]) – The command to run, along with any command-line flags. Example: [“python3”, “-v”]. If not provided, [“python3”] or [“python2”] will be chosen based on the py_version parameter.
• volume_size_in_gb (int or PipelineVariable) – Size in GB of the EBS volume to use for storing data during processing (default: 30).
• volume_kms_key (str or PipelineVariable) – A KMS key for the processing volume.
• output_kms_key (str or PipelineVariable) – The KMS key id for all ProcessingOutputs.
• max_runtime_in_seconds (int or PipelineVariable) – Timeout in seconds. After this amount of time Amazon SageMaker terminates the job regardless of its current status.
• base_job_name (str) – Prefix for processing name. If not specified, the processor generates a default job name, based on the training image name and current timestamp.
• sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain.
• env (dict[_str,_ str] or dict[_str,_ PipelineVariable]) – Environment variables to be passed to the processing job.
• tags (list[_dict[_str, str] or list[_dict[_str, PipelineVariable] ]) – List of tags to be passed to the processing job.
• network_config (sagemaker.network.NetworkConfig) – A NetworkConfig object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets.