MXNet Classes

MXNet Estimator

class sagemaker.mxnet.estimator.MXNet(entry_point, framework_version=None, py_version=None, source_dir=None, hyperparameters=None, image_uri=None, distribution=None, **kwargs)

Bases: Framework

Handle end-to-end training and deployment of custom MXNet code.

This Estimator executes an MXNet script in a managed MXNet execution environment.

The managed MXNet environment is an Amazon-built Docker container that executes 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 MXNetPredictor instance that can be used to perform inference against the hosted model.

Technical documentation on preparing MXNet scripts for SageMaker training and using the MXNet 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) – MXNet 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://aws.amazon.com/releasenotes/available-deep-learning-containers-images/.

  • py_version (str) – Python version you want to use for executing your model training code. One of ‘py2’ or ‘py3’. Defaults to None. Required unless image_uri is 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

    • 123412341234.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.

  • distribution (dict) –

    A dictionary with information on how to run distributed training (default: None). Currently we support distributed training with parameter server and MPI [Horovod]. To enable parameter server use the following setup:

    {
    'parameter_server':
    {
        'enabled': True
    }
}

    To enable MPI:

    {
    'mpi':
    {
        'enabled': True
    }
}

    Option parameters within mpi are processes_per_host and custom_mpi_options.

    {
    'mpi':
    {
        'enabled': True,
        'processes_per_host': 2,
        'custom_mpi_options': '-verbose --NCCL_DEBUG=INFO'
    }
}

  • **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, image_uri=None, **kwargs)

Create a SageMaker MXNetModel 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.

  • image_uri (str) –

    If specified, the estimator will use this image for 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

    • 123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0

    • custom-image:latest

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

Returns

A SageMaker MXNetModel object. See MXNetModel() for full details.

Return type

sagemaker.mxnet.model.MXNetModel

uploaded_code: Optional[UploadedCode]

MXNet Model

class sagemaker.mxnet.model.MXNetModel(model_data, role=None, entry_point=None, framework_version='1.4.0', py_version=None, image_uri=None, predictor_cls=<class 'sagemaker.mxnet.model.MXNetPredictor'>, model_server_workers=None, **kwargs)

Bases: FrameworkModel

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

Initialize an MXNetModel.

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.

  • framework_version (str) – MXNet version you want to use for executing your model training code. Defaults to 1.4.0. Required unless image_uri is provided.

  • py_version (str) – Python version you want to use for executing your model training code. Defaults to None. Required unless image_uri is provided.

  • image_uri (str or PipelineVariable) – A Docker image URI (default: None). If not specified, a default image for MXNet will be used. If framework_version or py_version are None, then image_uri is required. If image_uri is also None, then a ValueError will 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 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 (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 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 sagemaker.model.ModelPackage instance.

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.

Framework configuration is set in model environment variables.

Parameters

  • instance_type (str) – The EC2 instance type to deploy this Model to. For example, ‘ml.p2.xlarge’.

  • accelerator_type (str) – The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. For example, ‘ml.eia1.medium’.

  • 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, accelerator_type=None, 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. Used to determine device type (cpu/gpu/family-specific optimized).

  • accelerator_type (str) – The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model (default: None). For example, ‘ml.eia1.medium’.

  • 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

MXNet Predictor

class sagemaker.mxnet.model.MXNetPredictor(endpoint_name, sagemaker_session=None, serializer=<sagemaker.base_serializers.JSONSerializer object>, deserializer=<sagemaker.base_deserializers.JSONDeserializer object>, component_name=None)

Bases: Predictor

A Predictor for inference against MXNet Endpoints.

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

Initialize an MXNetPredictor.

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 (callable) – Optional. Default serializes input data to json. Handles dicts, lists, and numpy arrays.

  • deserializer (callable) – Optional. Default parses the response using json.load(...).

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

MXNet Processor

class sagemaker.mxnet.processing.MXNetProcessor(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 MXNet containers.

This processor executes a Python script in a managed MXNet execution environment.

Unless image_uri is specified, the MXNet 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 MXNet