sagemaker.mdx

title	Amazon SageMaker
description	Configure Amazon SageMaker with Continue to use deployed LLM endpoints for both chat and embedding models, supporting LMI and HuggingFace TEI deployments with AWS credentials

SageMaker can be used for both chat and embedding models. Chat models are supported for endpoints deployed with LMI, and embedding models are supported for endpoints deployed with HuggingFace TEI

Here is an example Sagemaker configuration setup:

```yaml title="config.yaml" name: My Config version: 0.0.1 schema: v1

models: - name: deepseek-6.7b-instruct provider: sagemaker model: lmi-model-deepseek-coder-xxxxxxx region: us-west-2 roles: - chat - name: mxbai-embed provider: sagemaker model: mxbai-embed-large-v1-endpoint roles: - embed

</Tab>
<Tab title="JSON">
```json title="config.json"
{
  "models": [
    {
      "title": "deepseek-6.7b-instruct",
      "provider": "sagemaker",
      "model": "lmi-model-deepseek-coder-xxxxxxx",
      "region": "us-west-2"
    }
  ],
  "embeddingsProvider": {
    "provider": "sagemaker",
    "model": "mxbai-embed-large-v1-endpoint"
  }
}

The value in model should be the SageMaker endpoint name you deployed.

Authentication will be through temporary or long-term credentials in ~/.aws/credentials under a profile called "sagemaker".

[sagemaker]
aws_access_key_id = abcdefg
aws_secret_access_key = hijklmno
aws_session_token = pqrstuvwxyz # Optional: means short term creds.