Hugging Face¶

Hugging Face Estimator¶

class sagemaker.huggingface.HuggingFace(py_version, entry_point, transformers_version=None, tensorflow_version=None, pytorch_version=None, source_dir=None, hyperparameters=None, image_uri=None, distribution=None, compiler_config=None, **kwargs)¶

Bases: sagemaker.estimator.Framework

Handle training of custom HuggingFace code.

This Estimator executes a HuggingFace script in a managed execution environment.

The managed HuggingFace environment is an Amazon-built Docker container that executes functions defined in the supplied entry_point Python script within a SageMaker Training Job.

Training is started by calling fit() on this Estimator.

Parameters

py_version (str) – Python version you want to use for executing your model training code. Defaults to None. Required unless image_uri is provided. If using PyTorch, the current supported version is py36. If using TensorFlow, the current supported version is py37.
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.
transformers_version (str) – Transformers version you want to use for executing your model training code. Defaults to None. Required unless image_uri is provided. The current supported version is 4.6.1.
tensorflow_version (str) – TensorFlow version you want to use for executing your model training code. Defaults to None. Required unless pytorch_version is provided. The current supported version is 2.4.1.
pytorch_version (str) – PyTorch version you want to use for executing your model training code. Defaults to None. Required unless tensorflow_version is provided. The current supported versions are 1.7.1 and 1.6.0.
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.
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. .. rubric:: 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, the following are supported: distributed training with parameter servers, SageMaker Distributed (SMD) Data and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI. To enable parameter server use the following setup:

{
    "parameter_server": {
        "enabled": True
    }
}

To enable MPI:

{
    "mpi": {
        "enabled": True
    }
}

To enable SMDistributed Data Parallel or Model Parallel:

{
    "smdistributed": {
        "dataparallel": {
            "enabled": True
        },
        "modelparallel": {
            "enabled": True,
            "parameters": {}
        }
    }
}

compiler_config (TrainingCompilerConfig) – Configures SageMaker Training Compiler to accelerate training.
**kwargs – Additional kwargs passed to the Framework constructor.

Tip

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

hyperparameters()¶: Return hyperparameters used by your custom PyTorch code during model training.

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 HuggingFaceModel 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. Defaults to None.
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 HuggingFaceModel constructor.

Returns

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

Return type

sagemaker.huggingface.model.HuggingFaceModel

Hugging Face Training Compiler Configuration¶

class sagemaker.huggingface.TrainingCompilerConfig(enabled=True, debug=False)¶

Bases: object

The configuration class for accelerating SageMaker training jobs through compilation.

SageMaker Training Compiler speeds up training by optimizing the model execution graph.

This class initializes a TrainingCompilerConfig instance.

Pass the output of it to the compiler_config parameter of the HuggingFace class.

Parameters

enabled (bool) – Optional. Switch to enable SageMaker Training Compiler. The default is True.
debug (bool) – Optional. Whether to dump detailed logs for debugging. This comes with a potential performance slowdown. The default is False.

Example: The following example shows the basic compiler_config parameter configuration, enabling compilation with default parameter values.

from sagemaker.huggingface import TrainingCompilerConfig
compiler_config = TrainingCompilerConfig()

DEBUG_PATH = '/opt/ml/output/data/compiler/'¶

SUPPORTED_INSTANCE_CLASS_PREFIXES = ['p3', 'g4dn', 'p4']¶

HP_ENABLE_COMPILER = 'sagemaker_training_compiler_enabled'¶

HP_ENABLE_DEBUG = 'sagemaker_training_compiler_debug_mode'¶

disclaimers_and_warnings()¶

Disclaimers and warnings.

Logs disclaimers and warnings about the requested configuration of SageMaker Training Compiler.

classmethod validate(image_uri, instance_type, distribution)¶

Checks if SageMaker Training Compiler is configured correctly.

Parameters

image_uri (str) – A string of a Docker image URI that’s specified to HuggingFace. If SageMaker Training Compiler is enabled, the HuggingFace estimator automatically chooses the right image URI. You cannot specify and override the image URI.
instance_type (str) – A string of the training instance type that’s specified to HuggingFace. The validate classmethod raises error if an instance type not in the SUPPORTED_INSTANCE_CLASS_PREFIXES list or local is passed to the instance_type parameter.
distribution (dict) – A dictionary of the distributed training option that’s specified to HuggingFace. SageMaker’s distributed data parallel and model parallel libraries are currently not compatible with SageMaker Training Compiler.

Raises

ValueError – Raised if the requested configuration is not compatible with SageMaker Training Compiler.

Hugging Face Model¶

class sagemaker.huggingface.model.HuggingFaceModel(role, model_data=None, entry_point=None, transformers_version=None, tensorflow_version=None, pytorch_version=None, py_version=None, image_uri=None, predictor_cls=<class 'sagemaker.huggingface.model.HuggingFacePredictor'>, model_server_workers=None, **kwargs)¶

Bases: sagemaker.model.FrameworkModel

A Hugging Face SageMaker Model that can be deployed to a SageMaker Endpoint.

Initialize a HuggingFaceModel.

Parameters

model_data (str) – The Amazon S3 location of a SageMaker model data .tar.gz file.
role (str) – An AWS IAM role specified with either the 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) – The absolute or relative path to the Python source file that 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. Defaults to None.
transformers_version (str) – Transformers version you want to use for executing your model training code. Defaults to None. Required unless image_uri is provided.
tensorflow_version (str) – TensorFlow version you want to use for executing your inference code. Defaults to None. Required unless pytorch_version is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
pytorch_version (str) – PyTorch version you want to use for executing your inference code. Defaults to None. Required unless tensorflow_version is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
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) – A Docker image URI. Defaults to None. If not specified, a default image for PyTorch will be used. If framework_version or py_version are None, then image_uri is required. If 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) – 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, response_types, inference_instances, transform_instances, 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)¶

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

Parameters

content_types (list) – The supported MIME types for the input data.
response_types (list) – The supported MIME types for the output data.
inference_instances (list) – A list of the instance types that are used to generate inferences in real-time.
transform_instances (list) – 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) – Model Package name, exclusive to model_package_group_name, using model_package_name makes the Model Package un-versioned. Defaults to None.
model_package_group_name (str) – Model Package Group name, exclusive to model_package_name, using model_package_group_name makes the Model Package versioned. Defaults to None.
image_uri (str) – Inference image URI for the container. Model class’ self.image will be used if it is None. Defaults to None.
model_metrics (ModelMetrics) – ModelMetrics object. Defaults to None.
metadata_properties (MetadataProperties) – MetadataProperties object. Defaults to None.
marketplace_cert (bool) – A boolean value indicating if the Model Package is certified for AWS Marketplace. Defaults to False.
approval_status (str) – Model Approval Status, values can be “Approved”, “Rejected”, or “PendingManualApproval”. Defaults to PendingManualApproval.
description (str) – Model Package description. Defaults to None.
drift_check_baselines (DriftCheckBaselines) – DriftCheckBaselines object (default: None).

Returns

A sagemaker.model.ModelPackage instance.

prepare_container_def(instance_type=None, accelerator_type=None)¶

A container definition with framework configuration 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.

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)¶

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.

Returns

The appropriate image URI based on the given parameters.

Return type

str

Hugging Face Predictor¶

class sagemaker.huggingface.model.HuggingFacePredictor(endpoint_name, sagemaker_session=None, serializer=<sagemaker.serializers.JSONSerializer object>, deserializer=<sagemaker.deserializers.JSONDeserializer object>)¶

Bases: sagemaker.predictor.Predictor

A Predictor for inference against Hugging Face Endpoints.

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

Initialize an HuggingFacePredictor.

Parameters

endpoint_name (str) – The name of the endpoint to perform inference on.
sagemaker_session (sagemaker.session.Session) – Session object that 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.