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Deploying your model on a SageMaker Endpoint

We recommend using the SageMaker Python SDK to deploy your model on SageMaker. Depending on which configuration format you are using (serving.properties file or environment variables), the steps are slightly different. You will need the following to deploy your model with LMI on SageMaker:

  • Model Artifacts (either HuggingFace Hub Model Id, or S3 URI pointing to model artifacts)
  • Instance Type
  • Container URI
  • Configuration File or Environment Variables

Option 1: Configuration - serving.properties

If your configuration is specified in a serving.properties file, we will need to upload this file to S3. If your model artifacts are also stored in S3, then you can either upload the serving.properties file under the same object prefix, or in a separate location. If you upload the serving.properties file under the same object prefix as your model artifacts, you should not specify option.model_id in the configuration.

For example, your directory containing model artifacts and serving.properties may look like this (flat structure):

my-lmi-model/
  - serving.properties
  - config.json
  - model-0001-of-0002.safetensors
  - model-0002-of-0002.safetensors
  - model.safetensors.index.json
  - tokenizer.json
  - tokenizer.model

If you upload the serving.properties to a different location than your model artifacts, make sure option.model_id points to the S3 URI of the object prefix (e.g. option.model_id=s3://my-bucket/my-model-artifacts/).

Here is the sample code you can use to upload your configuration to s3:

# Assumes that the serving.properties file is stored in a local folder called my-lmi-model/
# The my-lmi-model folder my also contain your model artifacts
aws s3 sync ./my-lmi-model s3://my-bucket/my-lmi-model

Here is the code to deploy your model on SageMaker using LMI:

import sagemaker

# Your IAM role that provides access to SageMaker and S3. 
# See https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex-role.html if running on a SageMaker notebook
iam_role = "my-sagemaker-role-arn"
# manages interactions with the sagemaker apis
sagemaker_session = sagemaker.session.Session()
# region is needed to retrieve the lmi container
region = sagemaker_session._region_name
# get the lmi image uri
# available frameworks: "djl-deepspeed" (for vllm, lmi-dist, deepspeed), "djl-tensorrtllm" (for tensorrt-llm), "djl-neuronx" (for transformers neuronx)
container_uri = sagemaker.image_uris.retrieve(framework="djl-deepspeed", version="0.27.0", region=region)
# create a unique endpoint name
endpoint_name = sagemaker.utils.name_from_base("my-lmi-endpoint")
# s3 uri object prefix under which the serving.properties and optional model artifacts are stored
# This is how we can specify uncompressed model artifacts
model_data = {
    "S3DataSource": {
        "S3Uri": "s3://my-bucket/my-lmi-model/",
        'S3DataType': 'S3Prefix',
        'CompressionType': 'None'
    }
} 
# instance type you will deploy your model to
instance_type = "ml.g5.12xlarge"

# create your SageMaker Model
model = sagemaker.Model(image_uri=container_uri, model_data=model_data, role=iam_role)
# deploy your model
model.deploy(
    instance_type=instance_type,
    initial_instance_count=1,
    endpoint_name=endpoint_name,
)

# Get a predictor for your endpoint
predictor = sagemaker.Predictor(
    endpoint_name=endpoint_name,
    sagemaker_session=sagemaker_session,
    serializer=sagemaker.serializers.JSONSerializer(),
    deserializer=sagemaker.deserializers.JSONDeserializer(),
)

# Make a prediction with your endpoint
outputs = predictor.predict({
    "inputs": "The meaning of life is", 
    "parameters": {"do_sample": True, "max_new_tokens": 256}
})

Option 2: Configuration - environment variables

If you are using environment variables for configuration, you will need to pass those configurations to the SageMaker Model object.

import sagemaker

# Your IAM role that provides access to SageMaker and S3. 
# See https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex-role.html if running on a SageMaker notebook
iam_role = "my-sagemaker-role"
# manages interactions with the sagemaker apis
sagemaker_session = sagemaker.session.Session()
# region is needed to retrieve the lmi container
region = sagemaker_session._region_name
# get the lmi image uri
# available frameworks: "djl-deepspeed" (for vllm, lmi-dist, deepspeed), "djl-tensorrtllm" (for tensorrt-llm), "djl-neuronx" (for transformers neuronx)
container_uri = sagemaker.image_uris.retrieve(framework="djl-deepspeed", version="0.27.0", region=region)
# create a unique endpoint name
endpoint_name = sagemaker.utils.name_from_base("my-lmi-endpoint")
# instance type you will deploy your model to
instance_type = "ml.g5.12xlarge"

# create your SageMaker Model
model = sagemaker.Model(
    image_uri=container_uri, 
    role=iam_role,
    # specify all environment variable configs in this map
    env={
        "HF_MODEL_ID": "<huggingface hub model id or s3 uri>",
        "OPTION_ROLLING_BATCH": "vllm",
        "TENSOR_PARALLEL_DEGREE": "max",
    }
)
# deploy your model
model.deploy(
    instance_type=instance_type,
    initial_instance_count=1,
    endpoint_name=endpoint_name,
)

# Get a predictor for your endpoint
predictor = sagemaker.Predictor(
    endpoint_name=endpoint_name,
    sagemaker_session=sagemaker_session,
    serializer=sagemaker.serializers.JSONSerializer(),
    deserializer=sagemaker.deserializers.JSONDeserializer(),
)

# Make a prediction with your endpoint
outputs = predictor.predict({
    "inputs": "The meaning of life is", 
    "parameters": {"do_sample": True, "max_new_tokens": 256}
})

Depending on which backend you are deploying with, you will have access to different generation parameters. To learn more about the API schema (Request/Response structure), please see this document.

Deploying your model on a SageMaker Endpoint with boto3

As an alternate to using the SageMaker python sdk, you could also use a sagemaker client from boto3. Currently, this provides with some additional options to configure your SageMaker endpoint.

Using ProductionVariants to customize the endpoint

The following options may be added to the ProductionVariants field to support LLM deployment:

  1. VolumeSizeInGB: option to attach a larger EBS volume if necessary.
  2. ModelDataDownloadTimeoutInSeconds: some models, due to their large size, take longer to be downloaded from S3 and untarred. With this option, you can increase the timeout for such downloads.
  3. ContainerStartupHealthCheckTimeoutInSeconds: Since LLMs may take longer to be loaded and ready to serve, the default /ping timeout of 5 minutes may not be sufficient. With this option, you can increase the timeout for the health check.

Follow this link for a step-by-step guide to create an endpoint with the above options: https://docs.aws.amazon.com/sagemaker/latest/dg/large-model-inference-hosting.html

Using ModelDataSource to deploy uncompressed model artifacts

The following options may be added to the ModelDataSource field to support uncompressed artifacts from S3:

"ModelDataSource": {
            "S3DataSource": {
                "S3Uri": "s3://my-bucket/prefix/to/model/data/", 
                "S3DataType": "S3Prefix",
                "CompressionType": "None",
            },

This mechanism is useful when deploying SageMaker endpoints with network isolation. Model artifacts will be downloaded by SageMaker and mounted to the container rather than being downloaded by the container at runtime.

If you use this mechanism to deploy the container, you should set option.model_id=/opt/ml/model in serving.properties, or OPTION_MODEL_ID=/opt/ml/model in environment variables depending on which configuration style you are using.

Follow this link for a detailed overview of this option: https://docs.aws.amazon.com/sagemaker/latest/dg/large-model-inference-uncompressed.html

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