For me it is working I deployed a
from sagemaker.huggingface import HuggingFaceModel
import sagemaker
role = sagemaker.get_execution_role()
# Hub Model configuration. https://huggingface.co/models
hub = {
'HF_MODEL_ID':'textattack/albert-base-v2-imdb',
'HF_TASK':'text-classification'
}
# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
transformers_version='4.6.1',
pytorch_version='1.7.1',
py_version='py36',
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
)
here is how invoked it using boto3
import boto3
import json
client = boto3.client('sagemaker-runtime')
request = {
'inputs': super_long_intput,
"parameters": {'truncation': True}
}
response = client.invoke_endpoint(
EndpointName="huggingface-pytorch-inference-2021-09-24-14-26-23-337",
ContentType="application/json",
Accept="application/json",
Body=json.dumps(request),
)
response['Body'].read().decode()
and a screenshot of that without {'truncation': True}
i get and error