> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Amazon SageMaker AI

> Deploy SGLang on Amazon SageMaker AI endpoints using the AWS Deep Learning Container.

Deploy SGLang on [Amazon SageMaker AI](https://aws.amazon.com/sagemaker/) endpoints using the
[AWS Deep Learning Container (DLC)](https://aws.github.io/deep-learning-containers/sglang/) for SGLang.
The SageMaker image variant accepts model configuration via environment variables and serves on port 8080.

This guide uses the pre-built DLC image. To build and deploy your own container instead, see
[Method 7: Run on AWS SageMaker](/docs/get-started/install#more-3) in the installation guide.

## Container image

AWS publishes pre-built, security-patched SGLang DLCs. The SageMaker GPU image is available from the
Amazon ECR registry (account `763104351884`) in each supported region. For example, in `us-west-2`:

```text theme={null}
763104351884.dkr.ecr.us-west-2.amazonaws.com/sglang:server-sagemaker-cuda-v1.0
```

For the full list of image tags, see the
[Available DLC Images](https://aws.github.io/deep-learning-containers/reference/available_images/) reference,
and for region-specific account IDs and supported regions, see
[Region Availability](https://aws.github.io/deep-learning-containers/reference/region_availability/).

## Specifying the model

The SageMaker image resolves the model in this order:

1. **`SM_SGLANG_MODEL_PATH` environment variable** — explicit Hugging Face ID or path.
2. **`/opt/ml/model`** — when SageMaker mounts model artifacts via `ModelDataUrl` or `ModelDataSource`,
   the entrypoint uses this path by default.

For gated models, also pass `HF_TOKEN`.

Any `SM_SGLANG_*` environment variable is converted to a `--<name>` SGLang server argument
(for example, `SM_SGLANG_CONTEXT_LENGTH=4096` becomes `--context-length 4096`).

## Deploy with the SageMaker Python SDK

```python theme={null}
from sagemaker.model import Model
from sagemaker.predictor import Predictor
from sagemaker.serializers import JSONSerializer

model = Model(
    image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/sglang:server-sagemaker-cuda-v1.0",
    role="arn:aws:iam::<account_id>:role/<role_name>",
    predictor_cls=Predictor,
    env={"SM_SGLANG_MODEL_PATH": "openai/gpt-oss-20b"},
)

predictor = model.deploy(
    instance_type="ml.g5.2xlarge",
    initial_instance_count=1,
    inference_ami_version="al2023-ami-sagemaker-inference-gpu-4-1",
    serializer=JSONSerializer(),
)

response = predictor.predict({
    "model": "openai/gpt-oss-20b",
    "messages": [{"role": "user", "content": "What is deep learning?"}],
    "max_tokens": 256,
})
print(response)

# Cleanup
predictor.delete_model()
predictor.delete_endpoint(delete_endpoint_config=True)
```

## Deploy with Boto3

```python theme={null}
import json
import boto3

sm = boto3.client("sagemaker")
smrt = boto3.client("sagemaker-runtime")

sm.create_model(
    ModelName="sglang-model",
    PrimaryContainer={
        "Image": "763104351884.dkr.ecr.us-west-2.amazonaws.com/sglang:server-sagemaker-cuda-v1.0",
        "Environment": {"SM_SGLANG_MODEL_PATH": "openai/gpt-oss-20b"},
    },
    ExecutionRoleArn="arn:aws:iam::<account_id>:role/<role_name>",
)

sm.create_endpoint_config(
    EndpointConfigName="sglang-config",
    ProductionVariants=[{
        "VariantName": "default",
        "ModelName": "sglang-model",
        "InstanceType": "ml.g5.2xlarge",
        "InitialInstanceCount": 1,
        "InferenceAmiVersion": "al2023-ami-sagemaker-inference-gpu-4-1",
    }],
)

sm.create_endpoint(EndpointName="sglang-endpoint", EndpointConfigName="sglang-config")
sm.get_waiter("endpoint_in_service").wait(EndpointName="sglang-endpoint")

resp = smrt.invoke_endpoint(
    EndpointName="sglang-endpoint",
    ContentType="application/json",
    Body=json.dumps({
        "model": "openai/gpt-oss-20b",
        "messages": [{"role": "user", "content": "What is deep learning?"}],
        "max_tokens": 256,
    }),
)
print(json.loads(resp["Body"].read()))

# Cleanup
sm.delete_endpoint(EndpointName="sglang-endpoint")
sm.delete_endpoint_config(EndpointConfigName="sglang-config")
sm.delete_model(ModelName="sglang-model")
```

## Model artifacts

When `ModelDataUrl` (or `ModelDataSource`) points to a tarball or S3 prefix, SageMaker mounts the contents
at `/opt/ml/model`. The entrypoint defaults `--model-path` to that location, so `SM_SGLANG_MODEL_PATH`
can be omitted:

```text theme={null}
model.tar.gz
├── config.json              # standard model files (Hugging Face layout)
├── tokenizer.json
└── *.safetensors
```

## Notes

* GPU deployments require `inference_ami_version` — the default SageMaker host AMI has incompatible NVIDIA
  drivers for CUDA 13 images. See the
  [ProductionVariant API reference](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ProductionVariant.html)
  for valid values.
* The endpoint exposes an OpenAI-compatible API, so the request body matches the SGLang server's
  `/v1/chat/completions` schema.