Deploy a custom model

After training completes, deploy your model for inference. You can deploy a custom model using either the CLI or the SDK.

Locate your model artifacts

Deploy using the CLI

Run the following command to deploy your custom model:

hyp create hyp-custom-endpoint \
    --version 1.0 \
    --env '{"HF_MODEL_ID":"/opt/ml/model", "SAGEMAKER_PROGRAM":"inference.py", }' \
    --model-source-type s3 \
    --model-location test-pytorch-job/model.tar.gz \
    --s3-bucket-name my-bucket \
    --s3-region us-east-2 \
    --prefetch-enabled true \ 
    --image-uri 763104351884.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:latest \
    --model-volume-mount-name model-weights \
    --container-port 8080 \
    --resources-requests '{"cpu": "30000m", "nvidia.com/gpu": 1, "memory": "100Gi"}' \
    --resources-limits '{"nvidia.com/gpu": 1}' \
    --tls-output-s3-uri s3://tls-bucket-inf1-beta2 \
    --instance-type ml.g5.8xlarge \
    --endpoint-name endpoint-custom-pytorch \
    --model-name pytorch-custom-model \
    

This command deploys the trained model as an endpoint named endpoint-custom-pytorch. The --model-location references the artifact path from the training job.

Deploy using the Python SDK

Create a Python script with the following content:

from sagemaker.hyperpod.inference.config.hp_custom_endpoint_config import Model, Server, SageMakerEndpoint, TlsConfig, EnvironmentVariables
from sagemaker.hyperpod.inference.hp_custom_endpoint import HPCustomEndpoint

model = Model(
    model_source_type="s3",
    model_location="test-pytorch-job/model.tar.gz",
    s3_bucket_name="my-bucket",
    s3_region="us-east-2",
    prefetch_enabled=True
)

server = Server(
    instance_type="ml.g5.8xlarge",
    image_uri="763104351884.dkr.ecr.us-east-2.amazonaws.com/huggingface-pytorch-tgi-inference:2.4.0-tgi2.3.1-gpu-py311-cu124-ubuntu22.04-v2.0",
    container_port=8080,
    model_volume_mount_name="model-weights"
)

resources = {
    "requests": {"cpu": "30000m", "nvidia.com/gpu": 1, "memory": "100Gi"},
    "limits": {"nvidia.com/gpu": 1}
}

env = EnvironmentVariables(
    HF_MODEL_ID="/opt/ml/model",
    SAGEMAKER_PROGRAM="inference.py",
    SAGEMAKER_SUBMIT_DIRECTORY="/opt/ml/model/code",
    MODEL_CACHE_ROOT="/opt/ml/model",
    SAGEMAKER_ENV="1"
)

endpoint_name = SageMakerEndpoint(name="endpoint-custom-pytorch")

tls_config = TlsConfig(tls_certificate_output_s3_uri="s3://tls-bucket-inf1-beta2")

custom_endpoint = HPCustomEndpoint(
    model=model,
    server=server,
    resources=resources,
    environment=env,
    sage_maker_endpoint=endpoint_name,
    tls_config=tls_config,
)

custom_endpoint.create()

Invoke the endpoint

Using the CLI

Test the endpoint with a sample input:

hyp invoke hyp-custom-endpoint \
    --endpoint-name endpoint-custom-pytorch \
    --body '{"inputs":"What is the capital of USA?"}'

This returns the model’s response, such as “The capital of the USA is Washington, D.C.”

Using the SDK

Add the following code to your Python script:

data = '{"inputs":"What is the capital of USA?"}'
response = custom_endpoint.invoke(body=data).body.read()
print(response)

Manage the endpoint

Using the CLI

List and inspect the endpoint:

hyp list hyp-custom-endpoint
hyp get hyp-custom-endpoint --name endpoint-custom-pytorch

Using the SDK

Add the following code to your Python script:

logs = custom_endpoint.get_logs()
print(logs)

Clean up resources

When you're done, delete the endpoint to avoid unnecessary costs.

Using the CLI

hyp delete hyp-custom-endpoint --name endpoint-custom-pytorch

Using the SDK

custom_endpoint.delete()

Next steps

You've successfully deployed and tested a custom model using SageMaker HyperPod. You can now use this endpoint for inference in your applications.