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Amazon SageMaker Deployment Script for Legion Coder 8M
This script demonstrates how to deploy the Legion Coder model to Amazon SageMaker
for production inference.
Requirements:
pip install sagemaker boto3
Usage:
python sagemaker_deploy.py
"""
import sagemaker
from sagemaker.huggingface import HuggingFaceModel
import boto3
# Configuration
ROLE_ARN = "arn:aws:iam::YOUR_ACCOUNT_ID:role/YOUR_SAGEMAKER_ROLE"
MODEL_ID = "dineth554/legion-coder-8m"
INSTANCE_TYPE = "ml.m5.large"
INSTANCE_COUNT = 1
def deploy_to_sagemaker():
"""
Deploy Legion Coder 8M to Amazon SageMaker.
This creates a SageMaker endpoint with the model ready for inference.
"""
# Initialize SageMaker session
sess = sagemaker.Session()
# Create Hugging Face Model
huggingface_model = HuggingFaceModel(
model_data=f"https://huggingface.co/{MODEL_ID}/resolve/main/model.safetensors",
transformers_version="4.36.0",
pytorch_version="2.1.0",
py_version="py310",
role=ROLE_ARN,
sagemaker_session=sess,
env={
"HF_MODEL_ID": MODEL_ID,
"HF_TASK": "text-generation",
"SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
"SAGEMAKER_PROGRAM": "inference.py"
}
)
# Deploy to SageMaker
predictor = huggingface_model.deploy(
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
endpoint_name="legion-coder-8m-endpoint"
)
print(f"Model deployed successfully!")
print(f"Endpoint name: legion-coder-8m-endpoint")
print(f"Instance type: {INSTANCE_TYPE}")
return predictor
def test_endpoint(predictor):
"""
Test the deployed endpoint with a sample prompt.
"""
test_payload = {
"inputs": "Write a Python function to calculate fibonacci numbers:",
"parameters": {
"temperature": 0.8,
"top_p": 0.95,
"top_k": 50,
"max_new_tokens": 200
}
}
response = predictor.predict(test_payload)
print("Test response:", response)
return response
def cleanup_endpoint(predictor):
"""
Clean up the SageMaker endpoint when done.
"""
predictor.delete_endpoint()
print("Endpoint deleted successfully.")
if __name__ == "__main__":
# Deploy the model
print("Deploying Legion Coder 8M to SageMaker...")
predictor = deploy_to_sagemaker()
# Test the endpoint
print("\nTesting endpoint...")
test_endpoint(predictor)
# Uncomment to clean up
# cleanup_endpoint(predictor)
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