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import json | |
import sagemaker | |
import boto3 | |
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri | |
try: | |
role = sagemaker.get_execution_role() | |
except ValueError: | |
iam = boto3.client('iam') | |
role = iam.get_role(RoleName='Moh-work')['Role']['Arn'] | |
# Hub Model configuration. https://huggingface.co/models | |
hub = { | |
'HF_MODEL_ID':'WizardLM/WizardMath-7B-V1.0', | |
'SM_NUM_GPUS': json.dumps(1) | |
} | |
# create Hugging Face Model Class | |
huggingface_model = HuggingFaceModel( | |
image_uri=get_huggingface_llm_image_uri("huggingface",version="1.0.3"), | |
env=hub, | |
role=role, | |
) | |
# deploy model to SageMaker Inference | |
predictor = huggingface_model.deploy( | |
initial_instance_count=1, | |
instance_type="ml.g5.2xlarge", | |
container_startup_health_check_timeout=300, | |
) | |
# send request | |
predictor.predict({ | |
"inputs": "create an E-Commernce platform in Next.js 13", | |
}) | |
predictor.delete_endpoint() | |