from sagemaker.huggingface import HuggingFaceModel import boto3

iam_client = boto3.client('iam') role = iam_client.get_role(RoleName='{IAM_ROLE_WITH_SAGEMAKER_PERMISSIONS}')['Role']['Arn']

Hub Model configuration. https://huggingface.co/models

hub = { 'HF_MODEL_ID':'lisdarr/fuck_shanghai_covid19', 'HF_TASK':'fill-mask' }

create Hugging Face Model Class

huggingface_model = HuggingFaceModel( transformers_version='4.17.0', pytorch_version='1.10.2', py_version='py38', env=hub, role=role, )

deploy model to SageMaker Inference

predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type='ml.m5.xlarge' # ec2 instance type )

predictor.predict({ 'inputs': "The answer to the universe is [MASK]." })