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import boto3
from pathlib import Path
import sagemaker
from sagemaker.huggingface import HuggingFaceModel
import logging
import tarfile
import os
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
def create_model_tar():
model_path = Path("models/customer_support_gpt")
tar_path = "model.tar.gz"
with tarfile.open(tar_path, "w:gz") as tar:
for file_path in model_path.glob("*"):
if file_path.is_file():
logger.info(f"Adding {file_path} to tar archive")
tar.add(file_path, arcname=file_path.name)
return tar_path
try:
# Initialize s3 client
s3 = boto3.client("s3")
bucket_name = 'customer-support-gpt'
# Create and upload tar.gz
tar_path = create_model_tar()
s3_key = "models/model.tar.gz" # Changed path
logger.info(f"Uploading model.tar.gz to s3://{bucket_name}/{s3_key}")
s3.upload_file(tar_path, bucket_name, s3_key)
# Initialize SageMaker session
sagemaker_session = sagemaker.Session()
role = 'arn:aws:iam::841162707028:role/service-role/AmazonSageMaker-ExecutionRole-20241109T160615'
# Verify IAM role
iam = boto3.client('iam')
try:
iam.get_role(RoleName=role.split('/')[-1])
logger.info(f"Successfully verified IAM role: {role}")
except iam.exceptions.NoSuchEntityException:
logger.error(f"IAM role not found: {role}")
raise
# Point to the tar.gz file
model_artifacts = f's3://{bucket_name}/{s3_key}'
print(f'Model artifacts: {model_artifacts}')
env = {
"model_path": "/opt/ml/model",
"max_length": "256",
"generation_config": '{"max_length":100,"temperature":0.7,"top_p":0.95,"top_k":50,"do_sample":true}'
}
try:
huggingface_model = HuggingFaceModel(
model_data=model_artifacts,
role=role,
transformers_version="4.37.0", # Explicit version
pytorch_version="2.1.0", # Matching your version
py_version="py310", # Keep py310
env=env,
name="customer-support-gpt"
)
logger.info("Starting model deployment...")
predictor = huggingface_model.deploy(
initial_instance_count=1,
instance_type="ml.m5.xlarge",
wait=True
)
logger.info("Model deployed successfully!")
except Exception as e:
logger.error(f"Error during model deployment: {str(e)}")
raise
except Exception as e:
logger.error(f"Deployment failed: {str(e)}")
raise
finally:
# Clean up the local tar file
if os.path.exists(tar_path):
os.remove(tar_path) |