Deploy a JumpStart model

You can deploy a pre-trained JumpStart model for inference using either the CLI or the SDK.

Using the CLI

Run the following command to deploy a JumpStart model:

hyp create hyp-jumpstart-endpoint \
  --version 1.0 \
  --model-id deepseek-llm-r1-distill-qwen-1-5b \
  --instance-type ml.g5.8xlarge \
  --endpoint-name endpoint-test-jscli

Using the SDK

Create a Python script with the following content:

from sagemaker.hyperpod.inference.config.hp_jumpstart_endpoint_config import Model, Server, SageMakerEndpoint, TlsConfig
from sagemaker.hyperpod.inference.hp_jumpstart_endpoint import HPJumpStartEndpoint

model=Model(
    model_id='deepseek-llm-r1-distill-qwen-1-5b'
)

server=Server(
    instance_type='ml.g5.8xlarge',
)

endpoint_name=SageMakerEndpoint(name='<endpoint-name>')

# create spec
js_endpoint=HPJumpStartEndpoint(
    model=model,
    server=server,
    sage_maker_endpoint=endpoint_name
)

Invoke the endpoint

Using the CLI

Test the endpoint with a sample input:

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

Using the SDK

Add the following code to your Python script:

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

Manage the endpoint

Using the CLI

List and inspect the endpoint:

hyp list hyp-jumpstart-endpoint
hyp get hyp-jumpstart-endpoint --name endpoint-jumpstart

Using the SDK

Add the following code to your Python script:

endpoint_iterator = HPJumpStartEndpoint.list()
for endpoint in endpoint_iterator:
    print(endpoint.name, endpoint.status)

logs = js_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-jumpstart-endpoint --name endpoint-jumpstart

Using the SDK

js_endpoint.delete()

Next steps

Now that you've trained a PyTorch model, deployed it as a custom endpoint, and deployed a JumpStart model using HyperPod's CLI and SDK, explore advanced features:

• Multi-node training: Scale training across multiple instances

• Custom containers: Build specialized training environments

• Integration with SageMaker Pipelines: Automate your ML workflows

• Advanced monitoring: Set up custom metrics and alerts

For more examples and advanced configurations, visit the SageMaker HyperPod GitHub repository.