TRTLLM rollingbatch ChatGLM3-6B deployment guide¶

In this tutorial, you will use LMI container from DLC to SageMaker and run inference with it.

Please make sure the following permission granted before running the notebook:

S3 bucket push access
SageMaker access

Step 1: Let's bump up SageMaker and import stuff¶

%pip install sagemaker --upgrade  --quiet

import boto3
import sagemaker
from sagemaker import Model, image_uris, serializers, deserializers

role = sagemaker.get_execution_role()  # execution role for the endpoint
sess = sagemaker.session.Session()  # sagemaker session for interacting with different AWS APIs
region = sess._region_name  # region name of the current SageMaker Studio environment
account_id = sess.account_id()  # account_id of the current SageMaker Studio environment

Step 2: Start preparing model artifacts¶

In LMI container, we expect some artifacts to help setting up the model - serving.properties (required): Defines the model server settings - model.py (optional): A python file to define the core inference logic - requirements.txt (optional): Any additional pip wheel need to install

%%writefile serving.properties
engine=MPI
option.model_id=THUDM/chatglm3-6b
option.tensor_parallel_degree=4
option.chatglm_model_version=chatglm3_6b
option.output_formatter=jsonlines
option.trust_remote_code=True

%%sh
mkdir mymodel
mv serving.properties mymodel/
tar czvf mymodel.tar.gz mymodel/
rm -rf mymodel

Step 3: Start building SageMaker endpoint¶

In this step, we will build SageMaker endpoint from scratch

Getting the container image URI¶

Large Model Inference available DLC

image_uri = image_uris.retrieve(
        framework="djl-tensorrtllm",
        region=sess.boto_session.region_name,
        version="0.27.0"
    )

Upload artifact on S3 and create SageMaker model¶

s3_code_prefix = "large-model-lmi/code"
bucket = sess.default_bucket()  # bucket to house artifacts
code_artifact = sess.upload_data("mymodel.tar.gz", bucket, s3_code_prefix)
print(f"S3 Code or Model tar ball uploaded to --- &gt; {code_artifact}")

model = Model(image_uri=image_uri, model_data=code_artifact, role=role)

4.2 Create SageMaker endpoint¶

You need to specify the instance to use and endpoint names

instance_type = "ml.g5.12xlarge"
endpoint_name = sagemaker.utils.name_from_base("lmi-model")

model.deploy(initial_instance_count=1,
             instance_type=instance_type,
             endpoint_name=endpoint_name,
             # container_startup_health_check_timeout=3600
            )

# our requests and responses will be in json format so we specify the serializer and the deserializer
predictor = sagemaker.Predictor(
    endpoint_name=endpoint_name,
    sagemaker_session=sess,
    serializer=serializers.JSONSerializer(),
)

Step 5: Test and benchmark the inference¶

predictor.predict(
    {"inputs": "tell me a story of the little red riding hood", "parameters": {"max_new_tokens":128, "do_sample":True}}
)

Clean up the environment¶

sess.delete_endpoint(endpoint_name)
sess.delete_endpoint_config(endpoint_name)
model.delete_model()